892 Remote Python Jobs in India

Job Title: Vice President – Technology (VP Tech)

Company: Timble Technologies Pvt. Ltd

Location: Arjan Garh, New Delhi (On-site)

Experience: 15+ Years

About Timble AI

Timble Glance is a high-growth AI-powered RegTech and enterprise B2B SaaS platform delivering mission-critical solutions to the BFSI sector. Our infrastructure powers 30+ enterprise-grade APIs handling digital identity management, real-time fraud mitigation, automated compliance, and document intelligence. We engineer high-concurrency, resilient systems designed for extreme throughput, sub-second latency, and bank-grade data security.

Role Overview

As the Vice President – Technology, you will be the chief technical strategist and engineering executive driving Timble AI’s technical vision, platform modernization, and AI innovation. You will own the end-to-end architectural roadmap, lead cross-functional engineering pods, and scale high-volume distributed systems that safeguard critical financial data. This role requires an executive who combines boardroom strategic gravitas with deep hands-on engineering credibility, capable of bridging rapid B2B SaaS product iteration with rigorous BFSI regulatory standards.

Key Responsibilities

·       Technology Vision & Architectural Strategy: Define and execute a multi-year engineering roadmap across core product suites (Identity Verification, Fraud Detection Engines, and Document Intelligence), balancing cutting-edge feature delivery with enterprise-grade stability.

·       Large-Scale Platform Engineering: Oversee the architecture and performance of 30+ high-scale APIs, ensuring 99.9% uptime, sub-second latency, fault-tolerant concurrency, and strict "efficiency by design" across multi-tenant cloud environments.

·       AI/ML & Intelligent Engineering: Drive the productionization of proprietary AI/ML and Generative AI pipelines—moving capabilities from experimental R&D into secure, low-latency, scalable BFSI workflows while advancing patent-pending intellectual property (IP).

·       Cloud Architecture & FinOps: Direct cloud economics and infrastructure investments across AWS and GCP, establishing containerization, microservice modularity, automated CI/CD pipelines, and robust disaster recovery frameworks.

·       Information Security & Regulatory Compliance: Enforce bank-grade security protocols, data privacy governance, and industry-mandated regulatory compliance (ISO 27001, SOC2, RBI/BFSI data localization guidelines) to protect sensitive enterprise data.

·       People & Engineering Culture Leadership: Attract, mentor, and scale a world-class engineering organization (from SDE-1 to Principal Architects and Engineering Managers); institute rigorous code reviews, automated testing benchmarks, and clear career ladders.

·       Executive & Stakeholder Alignment: Partner directly with the Founder, C-suite, and Product Leadership to translate complex technological initiatives into clear business outcomes, enterprise client trust, and scalable revenue growth.

Required Qualifications & Experience

·       Experience: 15+ years of progressive software engineering and technology leadership experience, with significant tenure leading engineering organizations in Fintech, RegTech, or high-scale B2B SaaS.

·       Education: B.Tech / M.Tech in Computer Science or related engineering field—Tier-1 institutes (IIT, IIIT, NIT) strongly preferred.

·       Core Technical Mastery: Deep hands-on expertise in no, distributed system design, microservices, asynchronous architectures, API gateways, and relational/NoSQL database engines (PostgreSQL, Redis, MongoDB).

·       Cloud & Infrastructure Rigor: Proven track record architecting enterprise systems on AWS/GCP, utilizing Docker, Kubernetes, Kafka/RabbitMQ, and modern APM/observability stacks.

·       AI Production Track Record: Demonstrated experience successfully deploying, monitoring, and scaling machine learning, computer vision, or NLP/LLM models in latency-critical production environments.

·       Governance & Delivery: Strong command of modern engineering methodologies (Agile, DevSecOps, TOGAF/ITIL principles) with a proven history of managing high-throughput, zero-downtime platforms.

Leadership Attributes

·       Technically Credible Executive: Capable of engaging in deep-dive architectural RFCs with engineering teams while articulating business impact and ROI to board members and enterprise BFSI clients.

·       Builder Mindset: Thrives in high-ownership, agile environments, successfully balancing rapid product velocity with uncompromised software quality and data integrity.

·       Force Multiplier: Focuses on institutionalizing robust engineering practices and developing leadership pipelines rather than relying on individual heroics.

·       https://timbleglance.com          

·       You can visit our website .

About Nexa Consultancy

Nexa Consultancy Inc is a Surrey, British Columbia (Canada) based company that helps other businesses set up everything they need to run and grow: dashboards, CRM, automation, reporting and marketing. We run our own operations on GoHighLevel, n8n and Make.com and build the same systems for our clients. Because this is ongoing work for our clients, we are looking for long-term people who will grow with us, not short-term contractors. We are hiring a full-time, remote Automation Developer in India to own this stack end to end.

What you will do

• Build, maintain and document automations in n8n and Make.com (lead intake, follow-up sequences, appointment booking, WhatsApp and email notifications, reporting).
• Own our GoHighLevel setup: pipelines, workflows, custom fields, calendars, forms, funnels and integrations.
• Connect tools through REST APIs and webhooks (GoHighLevel, Google Sheets, Gmail, WhatsApp, Meta lead forms, Cloudflare Workers, AI APIs).
• Write small scripts and serverless functions (JavaScript or Python) where a no-code step is not enough.
• Monitor scenarios, fix failures fast, and keep an eye on run quotas and costs.
• Build dashboards and reports so the team can see leads, follow-ups and conversions without asking.
• Turn a plain-English request from the Director into a working, tested automation.

What we are looking for

• 2 to 5 years of hands-on automation or integration work, with real n8n and Make.com scenarios you can show.
• Solid GoHighLevel experience (workflows, pipelines, snapshots, API). Other CRMs are a plus.
• Comfortable with REST APIs, webhooks, JSON, OAuth and debugging failed runs.
• Working JavaScript or Python for custom code steps; SQL or Google Sheets formulas are a plus.
• Clear written English. You will document what you build and explain it to non-technical teammates.
• Self-directed. This is a remote role with a small team; you will own outcomes, not just tickets.

Nice to have

• Cloudflare Workers, Zapier, Airtable, Notion, WhatsApp Business API, Meta or Google Ads integrations, OpenAI or Claude APIs.

Work setup

• Full-time, remote, from India.
• Long-term role. We want someone who stays, learns our clients' businesses and grows with the team.
• Must overlap with Canadian Pacific Time business hours for part of each day; exact schedule agreed at offer.
• Your own laptop, reliable high-speed internet and a smartphone are required.

Compensation

• INR 80,000 to 1,10,000 per month, based on experience.

How we hire

• Short screening call, then a practical exercise (build a small n8n or Make.com scenario), then a final interview with the Director.

About the Role

We are looking for a hands-on Applied AI / Full Stack Engineer to build AI-powered products and experiences. The ideal candidate should be comfortable working across AI, backend, and frontend and taking features from idea to production.

Key Responsibilities

• Build and deploy AI/LLM-powered applications and features.
• Develop AI agents, RAG pipelines, tool/function-calling workflows and integrations.
• Build scalable backend APIs and services.
• Develop frontend applications using React/Next.js.
• Integrate LLMs such as OpenAI, Claude, Gemini, etc.
• Work with databases, cloud platforms and production deployments.
• Collaborate with product and design teams and take ownership of features end-to-end.

Must-Have Skills

• 3–6 years of software engineering experience.
• Strong Python and/or TypeScript/Node.js.
• Strong React.js / Next.js / TypeScript experience.
• Hands-on experience with LLMs / Generative AI.
• Experience with RAG, AI Agents or tool/function calling.
• Experience building and deploying real-world products.
• Good understanding of APIs, databases and cloud deployment.

Good to Have

• LangChain / LangGraph / LlamaIndex
• PostgreSQL / Vector Databases
• AWS / GCP / Azure / Vercel
• Docker / CI/CD
• Experience in an early-stage startup or AI product company.

We are looking for builders who can take a problem, experiment, build, ship and improve it—not just candidates with AI keywords on their resume.

This is a backend role. You won't build screens, and you don't need frontend experience. The mobile team will add a language picker and pass a locale to the backend. Everything behind that is yours: the data model, content pipelines, LLM generation, retrieval, audio, notifications, and making sure all of it holds up in production.

What you'll build

1. The language data model

• Design how language lives in the database: user language preference, per-story translations, fallback chains (e.g. pt-BR → pt → en), and versioning so a translation is marked stale when its English source changes.
• stories.translations (jsonb) already exists and nothing reads it yet. Decide whether it stays, or whether we move to a dedicated story_translations table, and write down why.
• Everything ships as new, reversible migrations. We never edit an existing migration.

2. The content translation pipeline

• Our library has already been translated into several languages using the Anthropic Batch API, with a two-pass generate-then-review process. Move this into the repo so it's idempotent, re-runnable and cheap, and so it handles only the stories that changed.
• Build a glossary and style layer: Hebrew terms, names of sages and sources, and terms that must never be translated. Add automated QA checks (completeness, glossary adherence, length drift, broken formatting) plus a sampling workflow for native-speaker review.

3. Generation in the user's language

• Reflections, bridge text, the three practices (Echo, Naming, Walking) and Path arcs are generated by Claude through shared prompt modules in our Supabase edge functions. Make them produce native, natural output in the target language. That means writing in the language directly, not translating English after the fact. Our existing rules must still hold, e.g. each practice carries a Hebrew term with its meaning explained, now explained in the user's language.
• Build a per-language evaluation harness so we can check quality before a language goes live and catch regressions when prompts change.

4. Multilingual matching

• Today, language-validation detects the language of the user's input and blocks anything that isn't English. Replace that gate with detection and routing.
• Story matching uses OpenAI text-embedding-3-large over pgvector. Measure how well non-English input retrieves the right English-indexed stories. Then decide between cross-lingual matching, per-language embeddings, or a hybrid, and back the decision with numbers.

5. Audio

• Stories and reflections are read aloud by ElevenLabs voices. Audio is currently stored per voice (audio_urls), so it has to become per voice and per language. Pick voices for each language, and plan batch pre-rendering, storage, cost and backfill.

6. Server-side copy

• Move push notification copy (including our Shabbat-aware scheduling), transactional and lifecycle emails, and database-driven home-screen messages out of the code and into a locale-aware string system with proper plurals and date formats.

7. Safe rollout

• Several app versions are live at the same time, and older builds can't be changed. Every change you make must stay backward compatible: English stays the default, current RPC signatures stay the same, and each language can be switched on independently behind a flag.
• Track cost and quality for each language: tokens, TTS credits, fallback rate and generation failures.

Our stack

• Backend: Supabase: Postgres (RLS, RPCs, pgvector, jsonb, pg_cron) and 50+ Deno/TypeScript edge functions
• AI: Anthropic Claude (Messages and Batch APIs, prompt caching), OpenAI embeddings, ElevenLabs TTS
• Data/scripts: Python and Node
• Other: RevenueCat, Resend, Expo Push/FCM. The app is Expo/React Native, but you won't need to work in it.

You'll be a fit if you have

• 5+ years of backend engineering, with strong PostgreSQL: schema design, migrations on live data, RLS, stored functions, and performance tuning.
• Production TypeScript (Deno or Node) and solid Python for data pipelines.
• Shipped LLM features to production, beyond prototypes: prompt design, structured output, batch processing, cost control, and above all evaluation. You know how to tell whether output in a language you don't speak is good.
• Built backend internationalization before: locale negotiation, fallback chains, ICU/CLDR plurals and dates, and translation workflows with versioning.
• Worked with embeddings and vector search, and can design a retrieval evaluation.
• Care about keeping production running: backward compatibility, feature flags, and rollouts that can be undone.
• Write clearly. Decisions here get written down before they get built.

Nice to have

• Experience with ElevenLabs or other TTS pipelines.
• Supabase-specific experience.

We are looking for an experienced Software Engineer to join an AI engineering startup developing a document collection platform for accountants and professional services firms. 

Preference to candidates from Kerala, India.

The product eliminates the friction involved in gathering client files by automating document requests, centralising their collection, and organising incoming documents according to each organisation’s preferred folder structure.

The ideal candidate will be able to take ownership of work from start to finish, communicate clearly, and deliver high-quality solutions within tight timeframes.

What You’ll Work On

You’ll work with Python and Django daily, including models, views, templates, background jobs, and the wider product around them.

The frontend uses Django templates with HTMX and Alpine.js, built with Vite, TypeScript, and Tailwind CSS. The stack runs in Docker using PostgreSQL, Redis, RabbitMQ, and Celery.

You may also assist with ancillary projects, including custom integrations.

Project-based training will be provided.

Technology Stack

Backend: Python, Django, PostgreSQL, Celery, Redis and RabbitMQ

Frontend: Django Templates, HTMX, Alpine.js, Vite, TypeScript and Tailwind CSS

Infrastructure: Docker

Must Have

• Strong Python and Django skills
• Comfortable working with Docker
• Fluent written and spoken English
• Clear communication skills, including providing concise updates, asking honest questions, and writing information that others can act on
• Evidence of exceptional ability—not simply a list of tools, but something challenging you have built or solved

Preference will be given to candidates with at least three years of relevant professional experience.

Nice to Have

• Frontend experience with HTML, CSS and JavaScript
• Experience with HTMX, Alpine.js, TypeScript or Tailwind CSS
• Knowledge of PostgreSQL, Celery or pytest
• Experience with integrations, including APIs, OAuth and cloud storage
• Basic accounting knowledge

How to Apply

Please do not send a generic CV alone. Your application must include:

1. Evidence of exceptional ability: Describe a project, open-source contribution, production system or challenging problem you solved. Include a link to the repository, write-up or demo where possible. Focus on your personal contribution by detailing the specific parts of the project where you played a critical role and explaining precisely what you built or solved.
2. What you accomplished: Provide a short explanation of the outcome in your own words.
3. The hardest part: Explain the hardest part of the problem and how you dealt with it.
4. Your use of AI: Explain whether you use AI in your work and, if so, how you use it.
5. Your professional experience and interests: Include a brief paragraph summarising your professional experience and general interests.

Applications that do not include the above Croissant details above will not be considered.

What We Offer

• For the right candidate, salary will not be a constraint
• Project-based training
• A rewarding career with genuine opportunities for professional growth
• The opportunity to work on an innovative AI-driven product
• A remote, full-time position
• 
  

Job Details and Application Submission

Location: Remote

Employment Type: Full-time

Contract: One-year contract, with the possibility of extension based on satisfactory performance

Probationary Period: Six months

Preferred Experience: Three or more years

Position Overview

We are seeking a versatile Senior Full Stack & AI Agent Developer to architect, build, and

maintain end-to-end software solutions spanning web platforms, desktop applications, and

autonomous AI agents capable of interacting with and controlling these software systems.

The ideal candidate will bridge traditional engineering software with cutting-edge artificial

intelligence to automate data processing and enhance operational decision-making. While

not strictly required, a background or strong interest in the energy sector—specifically

drilling and completion operations—is highly desirable.

Key Responsibilities

• Full Stack Development: Design, develop, and deploy robust web applications and

native desktop software utilized by engineering and operational teams.

• AI Agent Engineering: Build, train, and integrate autonomous AI agents and LLM-

driven workflows capable of interpreting data, executing commands, and safely

controlling desktop and web-based software.

• Workflow Automation: Translate complex workflows into intuitive software features

and autonomous agent actions, minimizing manual data entry and operational

bottlenecks.

• Data Integration: Handle high-frequency data streams and integrate them seamlessly

into user interfaces and backend AI models.

• Architecture & Scalability: Ensure high performance, security, and scalability across

cloud infrastructure (AWS/Azure), local desktop environments, and potential edge

computing setups.

• Cross-Functional Collaboration: Work closely with domain experts and end-users to

translate field challenges into technical product requirements.

Required Qualifications & Experience

• Experience: Minimum of 5 years of professional software development experience,

with a proven track record of delivering production-ready web and desktop

applications.

• Programming Languages: Strong proficiency in Python, JavaScript/TypeScript, and at

least one compiled language (C#, C++, or Java).• Web & Desktop Frameworks: Hands-on experience with modern frontend

frameworks (React, Angular, or Vue.js), Node.js, and desktop application development

(Electron, WPF, Qt, or Tauri).

• AI & Agent Tooling: Demonstrated experience building AI agents using LLM APIs

(OpenAI, Anthropic), open-source models (Hugging Face), LangChain, LlamaIndex,

AutoGPT, or custom agent architectures.

• Automation & UI Control: Expertise in software control mechanisms using tools like

Selenium, Playwright, PyAutoGUI, Appium, or computer vision-based GUI automation to

allow AI agents to navigate software.

• Cloud, DevOps & Databases: Experience with Git, Docker, CI/CD pipelines, cloud

platforms (AWS/Azure/GCP), RESTful APIs, GraphQL, and relational/NoSQL databases.

Preferred Qualifications (Strong Plus)

• Industry Domain Expertise: Prior hands-on development experience within the oil and

gas sector, specifically focused on drilling, completions, rig operations, or subsurface

engineering software.

• Data & Protocols: Familiarity with oilfield data standards (e.g., WITSML, OPC-UA) and

time-series databases.

• Experience deploying AI models and agents in edge or low-connectivity environments

(such as offshore rigs or remote drilling sites).

• Familiarity with safety-critical software design and cybersecurity standards in

industrial control systems (ICS/SCADA).

• Degree in Computer Science, Software Engineering, Petroleum Engineering, or a related technical discipline.

Insurity’s Next Data Scientist:

We are seeking a Data Scientist to join our Predict team, focused on building and maintaining predictive models that support underwriting, claims, and audit use cases across Workers' Compensation and Commercial Auto. This role will be based in India and will play a key part in scaling our data science capabilities.

What Our Data Scientist Will Do: 

• Develop predictive models using GLM and machine learning techniques such as GBM, Random Forest, and XGBoost
• Perform feature engineering, selection, and transformation to optimize model performance
• Analyze structured and unstructured datasets to uncover insights and support model development
• Indentify and integrate third-party data sources to augment existing datasets and improve model accuracy
• Collaborate with product and engineering teams to integrate models into production environments
• Use AWS tools such as SageMaker and EC2 to build, train, and deploy models
• Document modeling decisions and communicate findings to technical and non-technical stakeholders
• Support model monitoring and performance tracking over time
•  

Who We’re Looking For:

• 2-5 years of experience in data science or predictive modeling
• Strong understanding of GLM and ML algorithms (GBM, XGBoost, Random Forest)
• Experience with Python and relevant libraries (scikit-learn, pandas, NumPy)
• Familiarity with AWS tools, especially SageMaker and EC2
• Experience with feature engineering and model evaluation techniques
• Ability to translate business problems into analytical solutions
• Strong communication skills and ability to work collaboratively in a cross-functional team
• Bachelor's or Master's degree in a quantitative field (Statistics, Mathematics, Computer Science, Data Science)
• Active listening
• Analytical and critical thinking
• Self-starter and quick learner
• Detail-oriented
• Ability to collaborate and work independently 
• Written and oral English communication
• Time management including work planning, prioritization, and organization 
• Sound judgement
• Ability to handle multiple priorities or tasks
• Flexible and adaptable

Role: Principal AI Architect — Multimodal Video Intelligence

Location: India Remote, with overlap with Singapore working hours

Employment Type: Full-time

Reporting to: Founder / CEO

Function: AI Architecture, Multimodal AI, Video Intelligence, Media Representation

About the Client

The client is building an AI-native media intelligence platform that transforms long-form video into structured, searchable, reusable and monetisable media intelligence.

The platform is not simply a video-clipping tool. We are developing a persistent intelligence layer for media, where video, audio, speech, text, objects, scenes, events, entities, emotions, narrative arcs and commercial signals are processed into a reusable representation that can support multiple downstream use cases, including:

• short-form clip generation;
• semantic search;
• scene and narrative understanding;
• contextual advertising;
• shoppable video;
• creator and content analytics;
• automated editing workflows;
• future media-intelligence APIs.

We are looking for a Principal AI Architect who can define and guide the AI architecture behind this platform.

Role Summary

The Principal AI Architect — Multimodal Video Intelligence will own the technical architecture for AI systems, including multimodal video understanding, persistent media representation, model orchestration, evaluation frameworks, and production AI design.

This is a hands-on architecture role. The ideal candidate can move between research papers, model selection, system design, data schemas, prototype review, engineering trade-offs, and implementation guidance.

You will work closely with the Founder / CEO, senior AI engineers, computer vision engineers, backend engineers and external vendors to convert the product and IP vision into a robust technical system.

Key Responsibilities

1. AI System Architecture

• Define the end-to-end AI architecture for long-form video understanding.
• Design the processing pipeline from video ingest to structured media intelligence.
• Define how vision, audio, speech, text, metadata and user signals should be fused.
• Design the architecture for reusable media intelligence rather than one-time clip generation.
• Ensure the system can support multiple downstream applications from the same processed media layer.

2. Persistent Media Representation

• Design persistent media representation layer across multiple levels, including frame, object, shot, scene, segment, entity, event and full-video levels.
• Define what intelligence must be stored permanently versus computed on demand.
• Design schemas for temporal, spatial, semantic, narrative and commercial metadata.
• Define provenance, confidence, model versioning and evidence-tracking requirements.
• Ensure the representation remains usable even when underlying AI models are replaced or upgraded.

3. Multimodal Model Strategy

• Select and evaluate appropriate models for video, image, audio, speech, OCR, entity extraction, scene understanding, action recognition, embeddings, reranking and LLM/VLM reasoning.
• Decide where to use open-source models, commercial APIs, fine-tuning or custom models.
• Define model interfaces so models can be swapped without breaking downstream systems.
• Guide model benchmarking for accuracy, latency, cost and scalability.
• Prevent over-dependence on any single model vendor or API.

4. Temporal and Narrative Intelligence

• Design approaches for understanding long-form video structure, including scenes, events, story arcs, character/entity continuity and engagement peaks.
• Define methods to identify clip-worthy moments across different content types.
• Support narrative scoring, highlight ranking, scene segmentation and coherence validation.
• Ensure that clips are not only visually interesting but contextually and narratively coherent.

5. Evaluation and Benchmarking

• Define objective evaluation frameworks for AI outputs.
• Build or guide creation of benchmark datasets and UAT criteria.
• Define metrics for clip quality, scene accuracy, entity continuity, timestamp alignment, hallucination control, ranking quality, retrieval precision and cost efficiency.
• Establish model and prompt evaluation processes.
• Create regression-testing methodology when models, prompts, schemas or scoring logic change.

6. Search, Retrieval and Knowledge Layer

• Design hybrid search architecture across transcript, visual events, metadata, embeddings and structured knowledge.
• Define when to use relational storage, vector databases, graph databases and object storage.
• Design queryable media intelligence for downstream APIs and applications.
• Support knowledge-graph or ontology-based representation where useful.
• Ensure retrieved outputs are evidence-backed and timestamp-grounded.

7. Production AI Architecture

• Work with AI engineers to convert architecture into deployable services.
• Guide decisions on batching, GPU inference, model serving, queues, retries, observability and cost controls.
• Review pipeline designs involving FFmpeg, GStreamer, DeepStream, TensorRT, Triton, ONNX, cloud services and model APIs.
• Define failure-handling, reprocessing, versioning and rollback mechanisms.
• Support scalable design without premature overengineering.

8. IP and Technical Differentiation

• Help translate AI architecture into defensible technical differentiation.
• Support patent-related technical disclosures where required.
• Identify what is proprietary versus commodity.
• Avoid building a generic wrapper over existing models.
• Ensure the architecture reinforces the core thesis of persistent, reusable media intelligence.

9. Team Guidance

• Provide technical direction to senior AI engineers and computer vision engineers.
• Review designs, experiments, evaluation results and architecture decisions.
• Mentor engineers without becoming a pure people manager.
• Help define technical milestones for the first 90, 180 and 365 days.
• Support hiring, technical interviews and vendor evaluation where needed.

Required Experience

The ideal candidate should have:

• 8+ years of AI/ML experience, with significant exposure to computer vision, video AI, multimodal AI, retrieval systems or production ML architecture.
• Strong experience designing AI systems, not only implementing isolated models.
• Hands-on experience with video understanding, temporal modelling, multimodal pipelines, VLMs, LLMs, embeddings, ranking or retrieval.
• Experience taking AI systems from prototype to production.
• Strong knowledge of Python and modern AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face or equivalent.
• Experience with model evaluation, benchmarking, error analysis and dataset design.
• Understanding of production architecture: APIs, queues, databases, cloud, model serving, observability and deployment trade-offs.
• Ability to work with founders and engineers in a high-ambiguity startup environment.

Strongly Preferred Experience

• Video understanding, action recognition, scene segmentation, event detection or video retrieval.
• Multimodal AI involving video, audio, speech, text and metadata.
• LLM/VLM orchestration for structured outputs.
• Prompt/version management, schema validation and hallucination control.
• Embedding search, vector databases, reranking and retrieval evaluation.
• Knowledge graphs, ontologies, entity resolution or temporal knowledge representation.
• Model serving using TensorRT, Triton, ONNX, vLLM, DeepStream or similar.
• Experience with long-form video, OTT, sports media, entertainment, creator platforms, advertising technology or social commerce.
• Experience contributing to patents, technical disclosures or investor diligence.

Technical Areas

The candidate should be comfortable discussing and making architecture decisions across:

• Computer vision;
• video AI;
• multimodal fusion;
• speech-to-text;
• OCR;
• image/video embeddings;
• VLMs and LLMs;
• semantic search;
• vector databases;
• graph databases;
• temporal reasoning;
• ranking and scoring systems;
• prompt orchestration;
• model evaluation;
• model versioning;
• data lineage;
• GPU inference;
• cloud AI deployment.

What This Role Is Not

This is not a role for someone who has only built:

• chatbots;
• basic RAG demos;
• LangChain prototypes;
• prompt-engineering workflows;
• simple OpenAI/Gemini API wrappers;
• dashboards over model outputs;
• classical computer vision demos without production architecture;
• MLOps pipelines without AI system-design depth.

The role requires architectural depth in AI systems, not just familiarity with AI tools.

First 90-Day Expectations

First 30 Days

• Review product thesis, patent direction, prototype plans and existing technical assumptions.
• Assess current team capability and architecture gaps.
• Define the first version of AI architecture.
• Identify immediate technical risks and validation priorities.

First 60 Days

• Deliver a detailed architecture document covering media representation, model stack, pipeline design, storage strategy, evaluation framework and implementation roadmap.
• Define the canonical media-intelligence schema.
• Define model-selection and benchmarking criteria.
• Guide senior engineers on first implementation milestones.

First 90 Days

• Help the team implement and validate the first working version of the persistent media-intelligence layer.
• Establish evaluation datasets and UAT metrics.
• Review prototype outputs and improve architecture based on evidence.
• Produce a 6-month AI roadmap with technical risks, milestones and resourcing needs.

Success Metrics

The Principal AI Architect will be successful if:

• They have a clear AI architecture that the engineering team can execute.
• The platform does not collapse into a generic clip-generation pipeline.
• The media representation is reusable across multiple use cases.
• Models, prompts and schemas are versioned and testable.
• AI outputs are measurable through objective benchmarks.
• Snehashish, Abhishek and other engineers have clear technical direction.
• The architecture supports both product execution and investor/IP defensibility.

Candidate Personality Fit

The right candidate should be:

• intellectually strong but practical;
• hands-on enough to review code and experiments;
• comfortable with ambiguity;
• willing to challenge assumptions with evidence;
• able to simplify complex AI architecture for engineers and investors;
• disciplined about evaluation, cost and production constraints;
• not attached to one model, tool or vendor;
• able to work in a founder-led early-stage startup. 

Role Overview

We are looking for a GCP Data Engineer with 10+ years of experience to design, develop, and optimize scalable cloud-based data solutions. The ideal candidate will have strong hands-on expertise in GCP, BigQuery, and advanced SQL, with experience building data pipelines and working with large-scale datasets.

Key Responsibilities

• Design and develop scalable data pipelines and ETL/ELT processes on GCP.
• Build, optimize, and maintain data solutions using Google BigQuery.
• Develop complex SQL queries for data transformation, aggregation, and analysis.
• Design efficient data models and optimize pipelines for performance, scalability, and cost.
• Integrate data from multiple sources and ensure data quality, reliability, and availability.
• Troubleshoot pipeline and data issues and drive continuous improvement.
• Collaborate with data architects, analysts, application teams, and business stakeholders.
• Follow best practices for cloud security, data governance, testing, and documentation.

Required Skills

• 8+ years of Data Engineering experience
• Strong hands-on experience with GCP, Django, and MongoDB
• Extensive experience with BigQuery
• Advanced SQL skills
• Strong understanding of ETL/ELT and data pipeline development
• Data modeling and data warehousing experience
• Experience handling large-scale datasets and performance optimization
• Strong problem-solving and communication skills

Good to Have

• GCP services such as Cloud Storage, Dataflow, Pub/Sub, Cloud Composer, or Cloud Functions
• Python or other data engineering languages
• Experience with data governance and security
• Agile development experience

We are looking for a senior, hands-on DevOps and Backend Integration Engineer to lead the production deployment and integration of an existing application ecosystem.

The platform currently includes a Flutter mobile application, a Laravel backend and admin panel, Node.js/AdonisJS APIs, a Python recommendation service, Redis, MySQL, Nginx, and AWS S3.

This is not a greenfield development role. The primary objective is to audit the existing system, establish a reliable production infrastructure, integrate all services, migrate traffic safely, and complete the production launch.

Responsibilities

• Audit the existing Hostinger environment, source repositories, application dependencies, database, Redis configuration, and deployment process.

• Design a secure and maintainable production architecture for Laravel, Node.js/AdonisJS, Python, Redis, MySQL, Nginx, AWS S3, and the Flutter application.

• Provision and configure Linux production servers, runtimes, SSH access, environment variables, permissions, SSL, and basic server security.

• Deploy and configure the Laravel application, admin panel, APIs, scheduled tasks, queues, and database connectivity.

• Deploy the Node.js/AdonisJS services with a reliable build, startup, logging, and process-management setup.

• Build a Git-based CI/CD pipeline for staging and production, including secrets management, deployment validation, and basic rollback handling.

• Configure Nginx as a reverse proxy and route traffic between Laravel and Node.js services using API versions and endpoint rules.

• Install and integrate Redis for agreed caching, queue, or session-management requirements.

• Configure AWS S3, IAM permissions, secure file access, and photo-upload integration to replace local server storage.

• Integrate the existing Python recommendation service with the Node.js backend, including timeout, retry, error-handling, and service-health scenarios.

• Support controlled migration of API traffic from Laravel to Node.js while preserving existing API contracts.

• Verify Flutter API compatibility, production environment configuration, authentication flows, media uploads, and backend-related integration issues.

• Conduct end-to-end, regression, and smoke testing across Flutter, Nginx, Laravel, Node.js, Redis, Python, MySQL, and AWS S3.

• Perform production cutover, validation, troubleshooting, and technical handover.

• Deliver clear architecture, deployment, routing, environment, rollback, and operational documentation.

Required Experience

• 5+ years of professional backend, DevOps, infrastructure, or platform-engineering experience.

• Strong hands-on Linux server administration and production deployment experience.

• Proven experience deploying and operating Laravel/PHP and Node.js applications.

• Strong knowledge of Nginx reverse proxy configuration, SSL, upstream services, and API routing.

• Experience creating CI/CD pipelines using GitHub Actions, GitLab CI, Bitbucket Pipelines, or a comparable platform.

• Practical experience with AWS S3, IAM permissions, secure media uploads, and cloud-storage integration.

• Experience with Redis, MySQL, background workers, queues, scheduled jobs, and application caching.

• Experience integrating Python services or machine-learning/recommendation APIs with Node.js applications.

• Good understanding of REST APIs, API versioning, authentication, secrets management, logging, monitoring, backups, and rollback procedures.

• Ability to independently investigate an existing codebase and resolve production integration problems.

• Strong written English and the ability to produce clear technical documentation.

Nice to Have

• Experience with AdonisJS.

• Experience supporting Flutter applications and mobile backend integrations.

• Experience migrating applications from shared hosting or Hostinger to a production cloud server.

• Experience with Google Maps Platform or location-based application services.

• Docker experience is helpful, although Kubernetes is not required.

Expected Deliverables

• Documented production architecture.

• Fully configured production infrastructure.

• Working Laravel, Node.js/AdonisJS, Python, Redis, MySQL, Nginx, and AWS S3 integrations.

• Functional staging and production CI/CD workflows.

• Tested Laravel and Node.js coexistence with controlled API routing.

• Successful end-to-end production deployment.

• Rollback, deployment, configuration, and operational documentation.

Engagement Details

• Contract duration: approximately 4–5 weeks.

• Work arrangement: remote.

• Availability: candidates should be able to begin shortly and provide regular written progress updates.

• The current Node.js APIs and recommendation algorithm already exist. Major new product features, algorithm redesign, extensive Flutter feature development, and database redesign are outside the initial scope.

How to Apply

Please include:

1. Examples of Laravel and Node.js systems you have deployed to production.

2. Details of a CI/CD pipeline and Nginx routing setup you personally implemented.

3. Your experience integrating Redis, AWS S3, and Python services.

Job Title: Full Stack AI Engineer

Location: Remote/Hyderabad

Experience Level: 3-5

Salary Range: 12-18LPA

Application Link:https://beyond.ciltriq.com/apply/BUILD

Description:

Join a team building AI-powered systems that solve complex business problems and automate operational workflows across document processing, voice agents, enterprise integrations, workflow automation, and multi-agent systems.

Strong full-stack foundations: frontend state management, asynchronous user experiences and performance; backend API design, authentication, data modelling, databases, queues and distributed systems.

Strong coding ability in Python and JavaScript or TypeScript, with practical experience in modern frontend frameworks and backend services.

Requirements:

- Design and build complete systems: frontend applications, backend services, APIs, databases, data pipelines and integrations with customer systems.

- Build multi-agent workflows with clear agent responsibilities, tool access, shared state, context management, routing, handoffs and coordination across sequential and parallel tasks.

- Make agent execution dependable through durable state, checkpoints, retries, timeouts, idempotency, recovery and human approval or review where needed.

- Deliver document-processing pipelines, voice agents and retrieval-based AI applications, connecting model outputs to useful actions in real business workflows.

- Own quality in production: automated tests, AI evaluations, guardrails, observability, access controls, deployments, incident response and clear documentation.

- Choose where AI adds value and where deterministic software is the better fit. Balance accuracy, latency, cost, security and maintainability.

- Improve reusable engineering foundations, review code and help other engineers grow as the team expands.

- A solid understanding of tool calling, structured outputs, retrieval, context and memory management, model selection and evaluation.

- Practical cloud and deployment experience, including containers, CI/CD, secrets management, logging, monitoring and production debugging.

- Ability to reason from first principles, investigate failures across system boundaries and communicate technical decisions clearly to customers and teammates.

- Useful additional experience: Document AI and OCR, real-time voice systems, enterprise integrations, agent protocols such as MCP, and orchestration frameworks.

- Useful additional experience: Mentoring engineers or building reusable platforms.

About Us:

CLOUDSUFI, a Google Cloud Premier Partner, is a global leading provider of data-driven digital transformation across cloud-based enterprises. With a global presence and focus on Software & Platforms, Life sciences and Healthcare, Retail, CPG, financial services and supply chain, CLOUDSUFI is positioned to meet customers where they are in their data monetization journey.

Our Values

We are a passionate and empathetic team that prioritizes human values. Our purpose is to elevate the quality of lives for our family, customers, partners and the community.

Equal Opportunity Statement

CLOUDSUFI is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified candidates receive consideration for employment without regard to race, colour, religion, gender, gender identity or expression, sexual orientation and national origin status. We provide equal opportunities in employment, advancement, and all other areas of our workplace. Please explore more at https://www.cloudsufi.com/

Role :

A Software Engineer who builds the tools this company runs on. You build agent loops, and the loops build the solutions. You work towards a Company Brain that anyone here can ask.3–5 years’ experience · Reports to the CFO · 

THE KEY SKILL

You build the agent loops that build the solutions. You will not write every automation by hand. You build the loops that

write them. Ship a prototype every week. You ship something every day.

You’ll be building a Company Brain with access control. One system that holds what the company knows about finance,delivery and people. Anyone can ask it a question. Each person sees only what they are cleared to see.

One hard filter. If you cannot write and debug production code, and have not done it before, please do not apply.

CORE RESPONSIBILITIES

• Work the backlog: You pick items off a live, ranked backlog. You learn each function by building inside it. There is no discovery phase. What you learn goes back into the backlog and changes what comes next.

• Build the product: You design, build and ship tools that people use every day. Reconciliation, MIS, the deal desk,quote to cash, or whatever the real bottleneck turns out to be. You choose the tools and frameworks.

• Wire the data: Connect the systems each team already uses, so that the same number means the same thing everywhere.

• Make it visible: You build live dashboards and alerts that leaders read on their own, instead of asking someone for a report.

• Keep it running: You own uptime and accuracy for everything you build. Anything that touches money or people needs a person in the loop.

THE STACK

• Build with: Python and TypeScript. You write production code. Frontier model APIs from Anthropic, OpenAI or Google, with tool calling and structured output. At least one agent framework. MCP to connect agents to internal systems.Postgres and pgvector, or something similar, for retrieval. You deploy on GCP, and you debug your own work.

• Work in agents daily: Claude Code, Cursor or something like them, as the way you write code every day. You should have a clear view on how to run the loop, and on when a person has to step in.

• Connect to: The systems we already run on for accounting, CRM, hiring and IT support, along with Google Workspace.Most of the work is getting them to agree with each other.

• Check what you ship: Anything that produces a number needs a way to catch it going quietly wrong. Test sets, regression checks, and alerts on the output as well as on the job.

WHAT GOOD LOOKS LIKE

• Something you built is running by week two, and someone is using it.

• By day 90, time spent on reconciliation or reporting is down by a number you can defend to the CFO.

• Every tool you ship has a named owner who is still using it 60 days later. That is the measure that counts.

• Leaders stop asking for numbers, because they can already see them.

• By the end of your first year, a first version of the Company Brain answers real questions about Finance, and each

person who asks sees only what they are cleared to see.

WHO THIS IS FOR

• You have built products: 3 to 5 years at a software product company, on a product with real users at scale. That means 100k+ monthly active users, or heavy daily use by a large enterprise customer base. You have owned code in production, in front of real users, long after it shipped.

• You ship alone: You are comfortable as the only engineer in the room, and the only person on call for what you built.

• You work out new ground fast: A domain you do not know is interesting to you. You start without waiting for a spec or an expert.

• You are fluent with agents: One person cannot cover a whole company by hand. You use agent loops heavily and youare good at it.

• You write and speak clearly: Half this job is pulling a process out of a finance or delivery lead and giving it back to them correctly. You work remotely, so this matters a great deal.

HOW WE WILL ASSESS

• A design problem: Live. We give you a function of the B2B company, and you design the system for it. We watch how you break down a domain you do not know, how you size it, and what you leave out on purpose.

• A build exercise: Live and screen-shared, on your own setup, with your own agents. You build the way you normally build. We watch how you run the loop, when you step in, and what you decide to skip.

• Your work and your questions: We talk about what you have shipped before. You ask us whatever you want.

Communication is not a separate round. All three sessions are live, and how clearly you explain your thinking is part of how we judge you.

WHERE IT LEADS

You report to the CEO and CFO from your first day. Your charter covers the whole company. Nothing sits between you and production. Very few engineering jobs offer all three at once, and that is why this one exists.

In 18 months you will know how this company really runs: the data, the money, and the gaps between teams. The rolethen changes shape to fit whatever the biggest open problem is by then.

Job Title : SDE 3 – Infrastructure Platform Engineer

Experience : 5.5 to 8.5 Years

Number of Positions : 2

Employment Type : C2H (Contract to Hire)

Work Mode : Remote during contractual period → 5 Days WFO after conversion

Contract Duration : 3 Months

Post-Conversion Location : Pune

Notice Period : Immediate Joiners / Serving Notice Period / Up to 15 Days preferred

(Candidates officially serving a 30-day notice period may also be considered if they are on the bench and have a negotiable joining date)

Role Overview :

We are looking for an experienced SDE 3 – Infrastructure Platform Engineer to design, build, and operate scalable, secure, and highly reliable cloud infrastructure and internal platform capabilities.

The ideal candidate will have strong hands-on experience in Cloud Infrastructure, Infrastructure as Code (IaC), CI/CD, Docker, Kubernetes, automation, observability, networking, and distributed systems.

Mandatory Skills : AWS / Azure / GCP, Terraform / CloudFormation, Kubernetes, Docker, CI/CD, Platform / Infrastructure Engineering, Python / Go / Java / Ruby, Networking, Cloud Security, Distributed Systems, Scalability & Reliability, Strong Coding & Automation.

Key Responsibilities :

• Design and maintain scalable, highly available infrastructure on AWS / GCP / Azure.
• Build and manage Infrastructure as Code (IaC) using Terraform, CloudFormation, or similar tools.
• Develop automation for infrastructure provisioning, deployments, monitoring, and operations.
• Manage and optimize Docker and Kubernetes workloads.
• Build internal platform tools to improve developer productivity and engineering efficiency.
• Implement monitoring, logging, alerting, and observability solutions.
• Participate in incident response, RCA, postmortems, and reliability improvements.
• Design and improve CI/CD pipelines and deployment automation.
• Contribute to system design, architecture discussions, scalability, security, and cost optimization.
• Collaborate with application, data, and product engineering teams.

Required Skills :

• 5.5 to 8.5 years of experience in Infrastructure / Platform Engineering or similar roles.
• Strong hands-on experience with AWS, GCP, or Azure.
• Strong expertise in Terraform / CloudFormation.
• Experience with CI/CD, Docker, and Kubernetes.
• Strong programming skills in at least one of:
• Python, Go, Java, or Ruby.
• Good understanding of networking, cloud security, distributed systems, scalability, and reliability.
• Experience working with production infrastructure and highly available systems.
• Strong troubleshooting and problem-solving skills.

Nice to Have :

• Experience with SRE practices and production on-call ownership.
• Experience in fintech, payments, banking, or transaction-heavy systems.
• Knowledge of cloud security, compliance, or FinOps/cost optimization.
• Experience building internal developer platforms or productivity tools.
• Previous product company experience.

Interview Process :

Round 1 : Take-Home Coding Assignment – Submit within 48 hours

Round 2 : Coding Assignment Discussion – 1 Hour

Round 3 : Technical Managerial Round – 30 Minutes

Note : The take-home coding assignment is mandatory. Candidates should be comfortable completing and submitting the assignment within 48 hours before proceeding.

Ideal Candidate :

Strong Platform / Infrastructure Engineer with hands-on experience in :

Cloud + Terraform / CloudFormation + Kubernetes + CI/CD + Programming + SRE / Production Operations

Pure DevOps profiles without strong coding and platform engineering experience are not preferred.

Golang Developer

Experience: 6-8 years

Employment Type: Contractual, Full-time

Desired Skill: Golang, GRPC, RestAPIs, AWS

Work Mode: Pune, India

Availability: Immediate Joiners

This is the contractual opportunity on a full time basis.

Must have skills:

● 6+years of Software Development experience

● 5+years of GoLang programming; Prefers additional proficiency in either Java or Python

● Knowledgeable in writing REST APIs

● Comfortable programming in production grade systems

● Experience with building HTTP based services

● Strong background of optimizing performance

● Familiarity with event-driven systems

● Experience dealing with highly concurrent, distributed architectures/systems.

Good to have skills:

• Exposure to relational databases
• Experience with Cloud Providers such as AWS is an advantage; cloud privileged access (CIEM, IAM roles, ephemeral credentials)
• Experience using Terraform to manage infrastructure as code would be an advantage
• Hands-on experience building or extending Privileged Access Management (PAM) capabilities — credential vaulting, secrets management, password rotation, just-in-time / time-bound privileged access
• Working knowledge of privileged session management and session recording — session proxying/brokering, keystroke and video capture, session replay, live monitoring and termination, tamper-evident audit trails
• Familiarity with remote access protocols and gateways — SSH, RDP, HTTPS/web-based sessions; protocol proxying and man-in-the-middle session interception

Job Description

• Lead Custom Odoo Application Design and Development: Provide leadership in designing and developing custom Odoo applications, ensuring alignment with business requirements and scalability.
• Collaborate Across Teams: Foster effective collaboration with cross-functional teams, including business analysts, project managers, and quality assurance, to identify and implement innovative solutions.
• Manage Odoo Application Lifecycle: Oversee the complete lifecycle of Odoo applications, from initial design and development to ongoing maintenance and upgrades for optimal performance.
• Technical Issue Resolution: Take a proactive role in troubleshooting and efficiently resolving technical issues during development and maintenance phases to ensure a seamless user experience.
• Mentor and Develop Team: Lead and mentor a team of developers, fostering their growth and development within the Odoo development domain.
• Stay Current with Odoo Framework Advances: Stay abreast of the latest developments in the Odoo framework and related technologies, incorporating new knowledge into ongoing projects.
• Drive Innovation and Best Practices: Champion innovation and best practices in Odoo development, driving the adoption of efficient and effective methodologies within the team.
• Client Interaction: Engage with clients to understand their business requirements, provide technical insights, and ensure the successful delivery of Odoo solutions.

Desired Skills:

• Bachelor’s degree in a related field or Computer Science.
• Minimum of 7 years of experience in software development, with a substantial focus on Odoo development.
• Strong proficiency in Python, XML, and SQL, showcasing advanced technical skills in designing and developing robust Odoo solutions.
• Proven leadership skills, with the ability to lead and mentor a team of developers effectively.
• Excellent problem-solving skills, demonstrating an ability to address complex challenges independently or collaboratively.
• 3wedStrong communication skills to facilitate effective interactions with team members, clients, and other stakeholders.
• Experience working with Agile methodologies and advanced proficiency in Git version control.

We are a San Francisco-based AI infrastructure company working with leading frontier AI labs to build post-training data and evaluation infrastructure for foundation models. We are hiring a Python Developer to create high-quality datasets, reinforcement learning environments, and benchmarking pipelines used to improve and evaluate state-of-the-art LLMs. This is a remote role with flexible working hours.

Responsibilities

* Create and curate datasets for LLM post-training (SFT, RLHF, RL, preference optimization).

* Build and maintain RL environments for agent evaluation.

* Develop Python tooling for dataset generation, validation, and transformation.

* Evaluate models on custom benchmarks and testing pipelines.

* Collaborate with research and engineering teams to deliver client-specific post-training datasets.

* Work with terminal-first development workflows and cloud infrastructure.

Required Skills

* Strong Python programming skills.

* Understanding of LLM fundamentals and post-training concepts (SFT, RLHF, RL).

* Experience working with structured data (JSON, CSV, YAML).

* Git, Linux/Unix command line, and solid software engineering fundamentals.

Good to Have

Experience with RAG, agentic AI systems, Hugging Face Transformers, LoRA/PEFT, LangChain or LlamaIndex, vector databases (FAISS, Qdrant, Milvus, Pinecone, Weaviate, ChromaDB), Docker, AWS/GCP, FastAPI/Flask, Bash, CLI tooling, model evaluation frameworks, benchmarking, and AI infrastructure.

Compensation

Base Salary: USD $1,250/month

Equity: ESOP/Equity package included.

Performance Bonuses: Up to USD $4,000/month (in addition to base salary).

Location

Remote (Worldwide)

Work Hours

Flexible, remote-first, asynchronous work environment.

How to Apply

Apply here: https://tally.so/r/wLReJG

Please complete the application form and submit the required details. Only shortlisted candidates will be contacted.

We are seeking a Senior Full Stack Engineer to join our team in a long-term contractor capacity to continue development of a production-grade platform hosted on AWS.

This application supports policy processing, third-party integrations, compliance workflows, reporting, and intelligent automation capabilities. The ideal candidate is a strong software engineer first, capable of contributing across the full stack while helping scale and evolve the platform.

This role requires someone who can step into an existing system, understand complex workflows quickly, and independently deliver high-quality solutions.

Responsibilities

• Design, develop, and maintain full-stack application features across frontend and backend systems

• Build and support integrations with third-party systems and APIs

• Develop workflow-driven processes using Temporal

• Build scalable APIs and backend services using Python

• Maintain and optimize relational databases using PostgreSQL

• Develop reporting and analytics capabilities using charting libraries

• Contribute to AI-enabled features and integrations within the platform

• Improve CI/CD pipelines and deployment processes

• Participate in architecture discussions and help shape long-term technical direction

• Work closely with business and technical stakeholders to deliver production-ready solutions

Required Qualifications

Engineering

• at least 7+ years of full stack software engineering experience

• Strong proficiency in Python

• Strong frontend development experience with modern web frameworks

• Strong backend API development experience

• Experience designing and building scalable applications

• Strong understanding of software architecture and best practices

• Experience working in complex, integrated systems

Workflow and Orchestration

• Proven experience with Temporal

• Experience building and managing workflow orchestration patterns

• Familiarity with asynchronous processing and event-driven systems

Database and Reporting

• Strong experience with PostgreSQL

• Strong SQL and data modeling experience

• Experience building reporting dashboards and analytics features

• Experience with charting libraries such as Chart.js, D3.js, or Plotly

DevOps

• Experience with CI/CD pipelines

• Familiarity with containerized deployments

• Experience with cloud environments and modern development workflows

Preferred Qualifications

• Experience in insurance, surplus lines, or compliance-based applications

• Experience integrating with third-party vendors and external APIs

• Experience with AI tooling, LLM integrations, and context engineering

• Experience building intelligent automation features

What We Are Looking For

• Self-driven and highly autonomous

• Strong problem-solving ability

• Comfortable with ownership and accountability

• Able to contribute with minimal supervision

• Strong communication skills in English

• Comfortable working U.S.-based business hours

Ideal Candidate

A senior full stack engineer who can quickly contribute to an active production system, own features end-to-end, and help expand a platform that sits at the center of complex business workflows and integrations. Send resume with projects and contact information.

We are seeking an experienced Solution Architect with deep expertise in Firewalls and Load Balancing, to lead the design, implementation, and optimization of our network strategy. The ideal candidate will possess a strong technical background, hands-on experience with Fortinet Firewalls (mandatory), F5 LTM (mandatory), Infra as code python/ansible (mandatory), Palo Alto Firewall (nice to have) and Cloudflare (nice to have), and a proven track record of delivering secure, scalable, and robust solutions in complex environments. 

Solution Architecture and Design :

• Architect for design, implementation and upgrade of firewalls solutions, • Analyze current business processes, IT infrastructure, and security requirements to develop security for our network solution.

• Develop high-level and detailed architecture diagrams, technical documentation, and integration designs.

• Ensure solutions align with enterprise security architecture, regulatory requirements (GDPR, SOX, etc.), and industry best practices.

Technical Competencies

Bachelor’s or master’s degree in computer science, Information Security, or related field.

• Strong expertise on Fortinet Firewalls - including rules management, FortiGate Managers, IPSec tunnels, firewall upgrade

• Strong expertise in F5 Load balancers, LTM module (APM nice to have)

• Strong Expertise on Infra As code python/Ansible, proven deployments of API based scripts to manage or reports Firewall or Load Balancers

• 10+ years of experience in complex network environments with 100+ firewalls

• Proven experience with Managing rules and managing upgrades on Fortigate environment (with FortiManager and FortiAnalyzer)

• Certification: Fortinet certification mandatory, F5 certification appreciated

• General shift business hours: from 10:30 AM to 7:30 PM IST

Role overview

The client is building a multimodal AI platform that processes multi-hour video, audio and text to generate structured insights, narratives and highlight workflows for broadcasters and media organisations.

We are seeking a Backend / Platform Engineer to design and build high-throughput media pipelines, robust APIs, and model-serving infrastructure that connect our AI engine (video perception + multimodal reasoning) to real products and customer environments.

This is not a CRUD‑only backend role.

You will work on:

• long‑running jobs
• distributed processing
• GPU inference orchestration
• storage for embeddings and metadata
• integration with AI models
• reliability and observability at scale

Key responsibilities

Media ingestion & processing pipelines

• Design and implement ingestion pipelines for multi‑hour video and audio content.
• Build microservices for frame extraction, audio processing, transcription integration and metadata generation.
• Handle long‑running, asynchronous jobs using queues, workers and robust retry strategies.
• Integrate with FFmpeg or similar tools for transcoding, segmenting and preparing media for AI models.

API & platform architecture

• Design and implement REST/gRPC APIs that expose AI model outputs (perception, multimodal alignment, narratives) to frontend and external systems.
• Define clear contracts for internal services and external integrations.
• Implement authentication, authorisation and rate‑limiting for platform endpoints.
• Ensure backward‑compatible API evolution as the product matures.

Model‑serving & AI integration

• Integrate with AI inference services (video models, multimodal models, LLM/VLM) running on GPUs or specialised infrastructure.
• Design request/response flows that handle large payloads, streaming outputs and structured results.
• Optimise throughput and latency for inference pipelines, including batching, caching and concurrency control.
• Collaborate closely with AI engineers to productionise models and debug end‑to‑end behaviour.

Storage, data models & performance

• Design data models to store embeddings, timelines, metadata, scene/shot boundaries, and narrative units.
• Work with appropriate storage technologies (SQL/NoSQL, object storage, search indices) based on access patterns.
• Implement indexing and query strategies for fast retrieval of segments, highlights and multimodal insights.
• Optimise performance for large datasets and high‑volume workloads.

Reliability, observability & operations

• Implement logging, metrics and tracing across services for debugging and monitoring.
• Set up health checks, circuit breakers and graceful degradation for critical services.
• Work with CI/CD pipelines to ensure safe, repeatable deployments.
• Collaborate on Kubernetes‑based deployments (or equivalent orchestration) for scaling services.

Requirements (must‑have)

Experience:

• 4–8 years in backend or platform engineering.
• At least 3 years working on distributed systems, high‑throughput services or complex pipelines (not just simple CRUD apps).

Languages & frameworks:

• Strong proficiency in Python or Node.js (one primary, both are a plus).
• Experience with at least one modern backend framework (FastAPI, Flask, Express, NestJS, etc.).

Distributed systems & pipelines:

• Hands‑on experience with queues and workers (e.g. Celery, RabbitMQ, Kafka, SQS, etc.).
• Experience building asynchronous, long‑running job pipelines.
• Understanding of idempotency, retries, backoff, and failure handling.

APIs & integration:

• Strong experience designing and implementing REST APIs (gRPC is a plus).
• Experience integrating with external services and handling network‑level failures.

Cloud & infrastructure:

• Experience deploying services on AWS, GCP or Azure (EC2/Compute Engine, S3/GCS, IAM, networking basics).
• Experience with Docker; exposure to Kubernetes is a strong plus.

Data & storage:

• Experience with SQL and at least one NoSQL store.
• Ability to design schemas and data models for performance and maintainability.

Engineering quality:

• Strong debugging skills across services and environments.
• Experience with unit/integration tests for backend systems.
• Clear, structured communication in English.

Nice‑to‑have

• Experience with media/video processing (FFmpeg, transcoding, segmenting).
• Experience with AI/ML model integration (serving models, handling inference requests).
• Experience with search/retrieval systems (e.g. Elasticsearch, vector databases).
• Experience with observability stacks (Prometheus, Grafana, OpenTelemetry).
• Experience working with remote teams across time zones.

What we are explicitly NOT looking for

To reduce noise and mismatches, we are not looking for:

• Pure CRUD‑only backend developers with no pipeline or distributed systems experience.
• Engineers who have only worked on small, single‑service apps without scale or complexity.
• Candidates who cannot explain trade‑offs in architecture, data modelling and reliability.
• Candidates who are uncomfortable with ownership of subsystems end‑to‑end.

Why join us

• Work on real, complex problems at the intersection of media, AI and distributed systems.
• Collaborate with senior AI engineers working on perception, multimodal fusion and narrative reasoning.
• Build the core platform that turns AI models into a usable product for broadcasters and media organisations.
• Operate with high ownership, clear expectations and direct access to the CTO.

Job Description – QA & Data Validation Engineer

Experience: 5–6 Years

Location: Pan India

Employment Type: Full-Time

Work Mode: Pan India / Remote or Hybrid as applicable

About the Role

We are looking for an experienced QA & Data Validation Engineer with 5–6 years of hands-on experience in data quality assurance, solution analysis, data validation, SQL, Python, PySpark, Azure Data Factory, Azure Synapse Analytics, and Power BI validation.

The ideal candidate will be responsible for validating large-scale data pipelines, performing source-to-target reconciliation, analyzing business rules, investigating data defects, and ensuring the accuracy, completeness, and consistency of data across source, staging, intermediate, and target systems.

The role requires strong analytical and problem-solving skills along with the ability to work closely with development, data engineering, business, and other stakeholders in an Agile delivery environment.

You will also contribute to the design, development, and maintenance of automated validation frameworks and utilities using Python, SQL, PySpark, Azure Data Factory, and Azure Synapse.

---

Key Responsibilities

1. QA & Solution Analysis

- Analyze business and technical requirements to understand data processing and validation needs.

- Participate in requirement analysis sessions and clarify functional and technical requirements with stakeholders.

- Review solution designs, data flows, mapping documents, interface specifications, and business rules.

- Validate that implemented solutions meet defined business and technical requirements.

- Identify gaps, inconsistencies, ambiguities, and potential data quality issues during requirement and solution analysis.

- Translate business requirements into detailed test scenarios, test cases, and validation conditions.

- Perform end-to-end validation of data processing workflows.

- Ensure data is accurately processed from source systems through intermediate layers to final outputs.

- Validate business rules and transformation logic implemented within data pipelines.

2. Test Planning & Execution

- Prepare comprehensive test strategies, test plans, test scenarios, and test cases for data-intensive applications.

- Execute functional, integration, regression, system, and data validation testing.

- Perform positive and negative testing for different data processing scenarios.

- Validate data pipelines across multiple environments, including staging, testing, and production.

- Identify test data requirements and prepare appropriate datasets for validation.

- Execute SQL queries to validate data processing and transformation results.

- Document test results, observations, defects, and validation evidence.

- Track testing progress and communicate status, risks, issues, and dependencies to stakeholders.

3. Data Validation & Reconciliation

- Perform detailed source-to-target data validation and reconciliation.

- Validate source, intermediate, staging, and output datasets.

- Perform record count validation between source and target systems.

- Verify data completeness, consistency, accuracy, and integrity.

- Validate data transformations against defined business rules.

- Perform field-level and record-level comparisons.

- Validate data types, formats, precision, scale, and null handling.

- Verify schema structure, layout, column names, and column sequence.

- Validate mandatory and optional fields.

- Identify missing, duplicate, truncated, or incorrectly transformed records.

- Analyze invalid records, rejected records, and exception datasets.

- Verify exception and reject-handling mechanisms.

- Compare production and staging data to identify discrepancies.

- Perform reconciliation between files, databases, and reporting layers.

- Validate data across different processing stages and identify the root cause of discrepancies.

4. File & Data Processing Validation

- Validate large-scale datasets across multiple file formats.

- Perform validation of:

 - CSV files

 - Delimited files

 - Fixed-width files

 - Excel files

 - Database tables

 - Structured and semi-structured datasets

- Validate file layouts, headers, delimiters, record formats, and column sequences.

- Verify file-level and record-level counts.

- Analyze source, intermediate, and final output files.

- Validate file-to-database and database-to-file reconciliation.

- Identify incomplete, corrupted, malformed, or invalid records.

- Verify data movement and transformation between different storage locations.

- Validate Azure-to-AWS file transfer processes.

- Ensure transferred files are complete and match the expected source datasets.

---

5. Defect Investigation & Root Cause Analysis

- Investigate data discrepancies and application/data pipeline defects.

- Perform detailed root cause analysis for data quality and validation failures.

- Analyze source data, transformation logic, pipeline execution, database records, and output datasets to identify defects.

- Collaborate with developers and data engineers to resolve identified issues.

- Reproduce defects and provide detailed technical evidence.

- Perform defect impact analysis.

- Conduct retesting and regression testing after defect resolution.

- Monitor recurring data quality issues and recommend preventive solutions.

- Maintain detailed defect documentation and validation results.

---

6. Python Development & Automation

- Develop Python scripts and utilities for data validation and reconciliation.

- Design, develop, and maintain reusable data validation frameworks.

- Automate repetitive data comparison and validation activities.

- Build automated utilities for:

 - Record count validation

 - Data completeness checks

 - Schema validation

 - Column sequence validation

 - Source-to-target comparison

 - Duplicate detection

 - Exception identification

 - Data quality checks

 - Automated reporting

- Develop Python-based validation and reporting utilities.

- Optimize Python scripts for processing large datasets.

- Maintain and enhance existing automation frameworks.

- Implement reusable validation components to improve testing efficiency and coverage.

---

7. SQL Development & Data Analysis

- Write complex SQL queries for data analysis and validation.

- Perform data extraction and comparison using SQL Server / SSMS.

- Validate source and target database records.

- Perform joins, aggregations, subqueries, CTEs, and analytical queries as required.

- Develop SQL queries to identify data mismatches, duplicates, missing records, and transformation issues.

- Validate database tables, schemas, columns, constraints, and relationships.

- Perform record count and reconciliation checks using SQL.

- Analyze SQL Server metrics databases.

- Validate data processing results against expected business rules.

- Troubleshoot data discrepancies using SQL queries.

---

8. PySpark & Large-Scale Data Processing

- Develop and execute PySpark notebooks for large-scale dataset processing and validation.

- Analyze large volumes of structured and semi-structured data.

- Perform data transformation and validation using PySpark.

- Compare large source and target datasets efficiently.

- Implement data quality and reconciliation checks using PySpark.

- Analyze exception, reject, and invalid datasets.

- Optimize data validation processes for large datasets.

- Work with Azure Synapse notebooks and data processing environments.

---

9. Azure Data Factory & Pipeline Testing

- Design and execute validation scenarios for Azure Data Factory (ADF) pipelines.

- Validate pipeline execution, data movement, transformations, and dependencies.

- Monitor pipeline runs and investigate failures.

- Validate source-to-target data movement through ADF.

- Develop and maintain test pipelines using Azure Data Factory.

- Verify pipeline parameters, triggers, activities, and execution results.

- Validate file ingestion and processing workflows.

- Perform end-to-end testing of data pipelines.

- Investigate pipeline-related data discrepancies and failures.

---

10. Azure Synapse Analytics

- Work with Azure Synapse Analytics for data validation and analysis.

- Develop and execute Synapse notebooks using PySpark.

- Validate datasets processed through Synapse pipelines and notebooks.

- Perform data quality and reconciliation checks within Synapse environments.

- Analyze large-scale datasets and processing results.

- Validate data movement between Azure storage, Synapse, databases, and reporting systems.

---

11. Azure Storage & Cosmos DB

- Validate data stored in Azure Storage Accounts and Containers.

- Verify file ingestion, processing, and output data.

- Perform file-level and content-level validation within Azure storage.

- Validate data processing workflows involving Azure Storage.

- Perform data validation in Azure Cosmos DB.

- Verify records, fields, formats, and data completeness within Cosmos DB.

- Investigate discrepancies between source files, Azure storage, databases, and Cosmos DB.

---

12. AWS S3 & Azure-to-AWS Validation

- Validate files stored in AWS S3.

- Perform source-to-target validation for files transferred between Azure and AWS.

- Verify file counts, file names, sizes, formats, and record counts.

- Compare source files with transferred S3 files.

- Validate data integrity after cloud-to-cloud file transfers.

- Investigate missing, incomplete, duplicate, or corrupted files.

- Support end-to-end validation of Azure-to-AWS data movement processes.

---

13. Metrics, Reporting & Power BI Validation

- Extract and validate source system metrics.

- Validate metrics stored in SQL Server databases.

- Perform reconciliation between source metrics, database metrics, and reporting outputs.

- Validate Power BI dashboards and reports against underlying source data.

- Verify report calculations, KPIs, measures, filters, and aggregations.

- Perform file-to-database-to-Power BI reconciliation.

- Validate data displayed in Power BI against SQL Server and source datasets.

- Identify discrepancies between backend data and dashboard results.

- Support reporting and analytics teams with data validation and troubleshooting.

---

14. Production Support & Job Monitoring

- Monitor scheduled data processing jobs and pipelines.

- Perform production validation and health checks.

- Analyze production failures and data discrepancies.

- Support incident investigation and resolution.

- Compare production and staging environments to identify differences.

- Validate production data after deployments and pipeline executions.

- Monitor ECG jobs and provide support for job execution and data processing issues.

- Perform post-production validation and reconciliation.

- Communicate critical production issues and risks to relevant stakeholders.

---

15. Agile Delivery & Stakeholder Collaboration

- Work effectively within an Agile/Scrum delivery environment.

- Participate in sprint planning, daily stand-ups, backlog refinement, sprint reviews, and retrospectives.

- Collaborate with Business Analysts, Developers, Data Engineers, DevOps teams, Product Owners, and other stakeholders.

- Provide timely updates on testing progress and issues.

- Participate in requirement clarification and solution discussions.

- Support release planning and production deployment activities.

- Track work items and defects using Rally.

- Ensure testing activities are aligned with sprint and release timelines.

---

Required Technical Skills

Mandatory Skills

- 5–6 years of experience in QA / Data Validation / Data Testing / Data Quality Engineering.

- Strong experience in SQL and data analysis.

- Hands-on experience with Python development and automation.

- Experience with PySpark and large-scale data processing.

- Strong experience with Azure Data Factory (ADF).

- Experience with Azure Synapse Analytics / Synapse Pipelines / Notebooks.

- Strong understanding of source-to-target data validation and reconciliation.

- Experience in data completeness, record count, schema, layout, and column validation.

- Experience in defect investigation and root cause analysis.

- Experience validating large datasets and multiple file formats.

- Experience with SQL Server / SSMS.

- Experience with Power BI dashboard/report validation.

- Strong understanding of data pipelines and ETL/ELT processes.

Cloud & Data Platform Experience

- Azure Data Factory

- Azure Synapse Analytics

- Azure Synapse Pipelines

- Azure Synapse Notebooks

- Azure Storage Accounts

- Azure Storage Containers

- Azure Cosmos DB

- Azure Privileged Identity Management (PIM)

- AWS S3

- Azure-to-AWS file transfer validation

---

Preferred Skills

- Experience developing automated data validation frameworks.

- Experience building automated reporting and reconciliation utilities.

- Knowledge of ETL/ELT testing methodologies.

- Experience working with very large datasets.

- Experience in production data validation and support.

- Knowledge of cloud-based data platforms.

- Experience with Power BI data reconciliation.

- Experience working in Agile environments.

- Experience with Rally or similar Agile project management tools.

- Familiarity with Microsoft Copilot and AI-assisted productivity/automation tools.

---

Key Responsibilities at a Glance

The successful candidate will be responsible for:

- Requirement analysis and clarification

- Business rule validation

- Test planning and execution

- Data quality and data validation

- Source-to-target reconciliation

- Record count and completeness validation

- Schema and layout validation

- Column sequence validation

- Exception and reject data analysis

- Production vs. staging comparison

- SQL-based data analysis

- Python automation

- PySpark development

- Azure Data Factory pipeline testing

- Azure Synapse validation

- Azure Storage validation

- Cosmos DB validation

- AWS S3 validation

- Azure-to-AWS file transfer validation

- Power BI dashboard validation

- SQL Server metrics validation

- Automated reporting

- Defect investigation and root cause analysis

- Production job monitoring and support

- Agile delivery and stakeholder collaboration

---

Candidate Profile

We are looking for a detail-oriented, analytical, and technically strong QA/Data Validation professional who can work independently on complex data validation assignments.

The candidate should be comfortable working with large datasets, writing SQL queries, developing Python automation, analyzing PySpark datasets, validating cloud-based data pipelines, and troubleshooting data discrepancies across multiple systems.

Strong communication and stakeholder management skills are essential, as the role requires regular collaboration with technical and business teams.

---

Education

Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, Data Science, or a related field is preferred.

Experience

5–6 years of relevant professional experience in QA, Data Testing, Data Validation, ETL Testing, Data Quality, Data Engineering QA, or a similar role.

Location

Pan India

Employment Type

Full-Time

Keywords

QA Engineer, Data QA, Data Validation, Data Testing, ETL Testing, Data Quality, SQL, Python, PySpark, Azure Data Factory, ADF, Azure Synapse, Synapse Analytics, Synapse Pipelines, Azure Storage, Cosmos DB, AWS S3, Power BI, SQL Server, SSMS, Data Reconciliation, Source-to-Target Validation, Data Pipeline Testing, ETL QA, Automation Testing, Data Analytics, Root Cause Analysis, Agile, Rally, Cloud Data Testing, Data Engineering QA.

SRE / Success Engineering role focused on production operations, reliability, AWS infrastructure, monitoring, incident management, and platform support for the ZT platform.

Core responsibilities include:

• Production monitoring and debugging of live systems.
• Incident investigation, troubleshooting, and problem resolution.
• AWS cloud infrastructure support and maintenance.
• Deployment and operational support activities.
• Supporting a 24x7 production environment.
• Working with GitHub-based development workflows.
• Technical debt remediation and platform improvements.
• Customer issue investigation and support.
• Security and compliance-related work, including FedRAMP initiatives.

Preferred Skills:

AWS (especially S3 and EC2)

Strong debugging and troubleshooting skills

Site Reliability Engineering (SRE) experience

GitHub experience

Basic software development skills

TypeScript/JavaScript knowledge

C# preferred

AI experience is a plus.

Candidate should be a hands-on engineer with strong AWS, SRE, operational ownership, production support, and debugging capabilities, rather than a pure application or full-stack developer.

Job Title : Senior Consultant – Full Stack Developer with AI

Experience : 5+ Years

Open Positions : 1

Location : Remote

Working Hours : 11:00 AM to 08:00 PM IST

Engagement : 6 to 8 Months Contract-to-Hire (C2H), with potential conversion to client payroll

Expected Joining : By the last week of August / 1st week of September

Role Overview :

Thoughtworks is looking for a Senior Consultant – Full Stack Developer with AI experience who can design and develop scalable full-stack applications while leveraging modern AI development tools and agentic AI capabilities.

The ideal candidate should have strong hands-on experience with Python, JavaScript / React.js, AWS Bedrock Agent Core, Docker, Kubernetes, and modern AI-assisted development frameworks and tools. Experience building MCP servers / tools, AI skills, or integrations using tools such as Cursor, Claude Code, Codex, or GitHub Copilot will be highly valuable.

The candidate should be comfortable working across application development, AI integration, cloud technologies, and containerized environments.

Mandatory Skills :

Python, React.js / JavaScript, AWS Bedrock Agent Core, Docker, Kubernetes, MCP / AI Skills, Cursor / Claude Code / Codex / GitHub Copilot, Full-Stack Development.

Key Responsibilities :

• Design, develop, and maintain scalable full-stack applications using modern development practices.
• Build backend services and APIs using Python and related frameworks.
• Develop responsive and scalable frontend applications using ReactJS or other JavaScript frameworks.
• Design and implement AI-powered solutions using AWS Bedrock Agent Core.
• Build and integrate AI agents, tools, skills, and workflows into enterprise applications.
• Develop and work with MCP (Model Context Protocol) servers, tools, or integrations.
• Leverage AI-assisted development platforms and coding tools such as Cursor, Claude Code, Codex, or GitHub Copilot.
• Containerize applications and services using Docker.
• Deploy, manage, and troubleshoot containerized workloads using Kubernetes.
• Collaborate with cross-functional teams to understand business requirements and translate them into scalable technical solutions.
• Follow engineering best practices around code quality, testing, security, performance, and maintainability.
• Contribute to CI/CD and cloud deployment processes; DevOps experience will be an added advantage.
• Participate in technical discussions, architecture decisions, code reviews, and project delivery.

Mandatory Skills :

• 5+ years of relevant software development experience.
• Strong hands-on experience with Python.
• Strong experience with ReactJS or another modern JavaScript framework.
• Hands-on experience with AWS Bedrock Agent Core.
• Strong experience with Docker.
• Hands-on experience with Kubernetes.
• Experience building MCP servers / tools, AI skills, or similar AI integrations.
• Experience using AI-assisted coding/development tools such as :
• Cursor
• Claude Code
• Codex
• GitHub Copilot
• Strong understanding of full-stack application development.
• Good understanding of API development, application architecture, and cloud-based solutions.

Nice to Have :

• Experience with DevOps practices and CI/CD pipelines.
• Experience with AWS cloud services beyond Bedrock.
• Experience with infrastructure automation and deployment.
• Experience building production-grade GenAI / Agentic AI applications.
• Experience with LLM integrations, AI agents, tools, and function calling.

Project Expectations :

Candidates should be able to explain at least one recent project in detail, including :

• Problem Statement : What business / technical problem were you solving ?
• Architecture & Approach : How did you design the solution ?
• Key Contributions : What did you personally build or own ?
• AI / Agent Implementation : How did you use AWS Bedrock Agent Core, MCP, or AI development tools ?
• Technology Stack : Python, React.js / JavaScript, AWS, Docker, Kubernetes, etc.
• Challenges : What were the major technical challenges ?
• Outcomes & Metrics : What measurable impact did the solution deliver, such as performance improvement, cost reduction, automation, productivity improvement, or reduced development time ?

Interview Process :

1. Round 1 – GT Technical Interview : 60 minutes
2. Round 2 – Client Technical Interview : 60 minutes
3. Round 3 – Project Round : 60 minutes
4. Additional Client Round : May be conducted on a case-by-case basis

Key Hiring Priorities :

Highest priority : Candidates with genuine hands-on experience in AWS Bedrock Agent Core + Python + React / JavaScript + Docker + Kubernetes + MCP / AI skills and practical experience using modern AI coding/agent development tools.

Note : Candidates should demonstrate hands-on implementation experience rather than only theoretical knowledge or exposure to the above technologies.

About Nexora Group

Nexora Group is a forward-thinking technology and innovation company focused on leveraging Artificial Intelligence, Data Science, and emerging technologies to solve real-world business challenges. We provide opportunities for aspiring professionals to gain hands-on experience, work on impactful projects, and develop industry-relevant skills in a collaborative environment.

Internship Overview

We are looking for enthusiastic and motivated Data Science with AI Interns to join our growing team. This internship is designed for students and recent graduates who are passionate about data analytics, machine learning, artificial intelligence, and data-driven decision-making.

The selected candidates will work alongside experienced professionals on real-world datasets, AI models, and business intelligence projects while gaining practical exposure to industry-standard tools and technologies.

Key Responsibilities

• Collect, clean, and preprocess structured and unstructured datasets.
• Perform exploratory data analysis (EDA) and generate actionable insights.
• Assist in developing and deploying machine learning and AI models.
• Work with Python, SQL, and data visualization tools.
• Create dashboards, reports, and data-driven presentations.
• Support predictive analytics and model evaluation activities.
• Collaborate with cross-functional teams on AI-driven projects.
• Research emerging trends in Data Science, Machine Learning, and Generative AI.
• Document project findings and maintain technical reports.

Required Skills

• Basic understanding of Data Science and Machine Learning concepts.
• Knowledge of Python and data analysis libraries (Pandas, NumPy, Matplotlib, Scikit-learn).
• Familiarity with SQL and database concepts.
• Understanding of AI, Generative AI, and Large Language Models (LLMs) is a plus.
• Strong analytical and problem-solving skills.
• Good communication and teamwork abilities.
• Eagerness to learn and adapt to new technologies.

Eligibility

• Undergraduate or postgraduate students pursuing Computer Science, Data Science, AI, IT, Statistics, Mathematics, or related fields.
• Recent graduates looking to gain practical industry experience.
• Candidates with personal projects, certifications, or relevant coursework will be preferred.

What You'll Gain

• Hands-on experience with real-world AI and Data Science projects.
• Mentorship from industry professionals.
• Exposure to modern AI tools and technologies.
• Internship Certificate upon successful completion.
• Letter of Recommendation (based on performance).
• Opportunity for a Pre-Placement Offer (PPO) for outstanding performers.
• Professional networking and career development opportunities.

Essential Duties and Responsibilities:

• Build new automations in Python: API integrations, data pipelines, scheduled jobs, and process replacements scoped with operating partners.

• Maintain the existing Power Automate estate, both unattended cloud and desktop flows. Triage failures, repair flows, and keep unattended runs healthy on the bot-server farm. Operate the Power Platform space around them: environments, solutions, connection references, and pipeline-managed deployments.

• Migrate Power Automate flows to Python where the economics favor it. Retire flows rather than patching them indefinitely.

• Integrate systems over REST APIs. Handle JSON and XML transformation, authentication (OAuth, service principals), and error handling that survives flaky endpoints.

• Author SQL queries, tables, and stored procedures that support automations.

• Operate what you build. Instrument jobs with logging, monitoring, and alerting so failures surface before the business notices them. Write runbooks.

• Improve how automations run. Today they run as scheduled jobs on VMs. Help evaluate and move toward containerized or Azure-native execution (Functions, Container Apps) where it reduces operational load.

• Use AI coding tools as a core part of daily development, within company governance and review standards.

• Document what you build so the next engineer, or an operating partner, can understand and extend it.

Knowledge, Skills and Abilities:

• Python proficiency: clean scripting, packaging, error handling, structured logging, and enough testing to trust a job running unattended at 2 a.m.

• Power Automate strength across cloud and desktop flows: able to read, debug, and repair complex unattended flows built by someone else, plus the platform administration around them. You do not need to love the platform. You do need to support it capably, including solo coverage when other developers are out.

• REST API integration experience, including authentication patterns and rate-limit handling.

• SQL Server competence: comfortable writing T-SQL and authoring queries, tables, and stored procedures through a reviewed, versioned release process.

• Working knowledge of Azure: DevOps pipelines at minimum; Functions, Container Apps, or AKS exposure a plus.

• Daily fluency with AI-assisted development. You should be able to describe, in concrete detail, how you structure work with an agentic coding tool: what you delegate, what you review, where it fails, and how you catch it.

• PowerShell and shell scripting for glue work on Windows and Linux hosts.

• Production instincts: idempotent jobs, retries with backoff, alerting thresholds that page on real problems and stay quiet otherwise.

• Plain written and verbal communication. You will work directly with non-technical process owners who need to understand what an automation does and what to do when it stops.

Training and Experience:

• 3 to 5 years in automation engineering, RPA, or software engineering roles with automations shipped to production and operated afterward.

• A track record you can walk through: what you built, what broke, and what you changed.

• Demonstrated, current use of AI coding tools in real work. Candidates will be asked to describe their workflow in specifics; vague answers end the conversation.

Role Summary

We are looking for an accomplished PowerBi Solution architect to architect and drive scalable, insight-rich analytics solutions across the enterprise. This role sits at the intersection of data engineering, business intelligence, and solution architecture—ideal for a candidate with mastery of modern visualization tools, modeling strategies, and end-to-end data integration.

Key Responsibilities

● Architect and implement scalable Power BI solutions that span data modeling, integration, visualization, and performance tuning

● Design and lead the development of enterprise-grade data models using Star, Snowflake, and composite architecture

● Lead the integration of cloud-based data warehouse platforms including Oracle ADW and Snowflake

● Lead the development of scalable, interactive dashboards using Power BI, driving self-service analytics across business units

● Develop ETL and ELT pipelines to ingest and transform structured, semi-structured, and API-driven data sources

● Implement automated data ingestion and transformation using Python and shell scripting for performance and reliability

● Handle and harmonize data from diverse API sources across applications, platforms, and third-party services

● Deliver interactive dashboards and storytelling experiences tailored for executives, analysts, and operational teams

● Optimize semantic layers, DAX calculations, and deployment pipelines for reusability and governance

● Mentor BI developers and analysts, setting architectural standards and fostering collaboration across analytics teams

● Collaborate with cross-functional teams to translate strategic business goals into robust analytics solutions

Required Qualifications

● 8+ years in Power BI development and analytics Engineering, with 3+ years in architecture leadership roles

● Strong command of DAX, M Query, and performance tuning best practices

● Deep experience in data modeling (Star, Snowflake, composite) and cross-platform architecture

● Hands-on expertise in Oracle ADW, Snowflake, and API-based data ingestion

● Proficiency in Python, shell scripting, and automation of analytics pipelines

● Strong background in ETL/ELT development, data quality assurance, and deployment orchestration

● Demonstrated success in solution architecture across cloud ecosystems such as Azure, Oracle OCI

Preferred Skills

● Experience with Power BI Premium capacity management and deployment pipelines

● Experience with DAX, M language, and performance tuning technique

● Experience integrating third-party BI tools, data lakehouses, and stream processing services

● Effective communicator capable of translating technical architecture into business value

● Exposure to MS Fabric and Azure Data Factor

Software Development Engineer

What you'll do

Be the dedicated engineer for one of our clients. Learn their business, find what to automate, and build it.

Build automations, integrations, and web apps on whatever the client runs business platforms like the Zoho suite, cloud services like Azure, and custom apps where needed.

Work with APIs every day: connect systems, move data, make tools talk to each other.

Own work end to end: hear the problem from the stakeholder, build the solution, ship it, demo it, support it.

Resolve client tickets and bugs fast. Keep their systems and data healthy.

Write things down: clear docs, clear updates, clear commit history.

What we're looking for

2–5 years shipping software that works and being able to read it, debug it, and stand behind it.

Strong with REST APIs and integrations between systems.

Happy working inside business platforms (Zoho, Salesforce, or similar). Much of the leverage in this role lives there, not in custom code.

You can sit in a call with a non-technical client, understand the real problem, and come back with a working solution.

Self-driven. You'll often be the only engineer on a problem.

Why you'll love this role

Full ownership from first call to delivery you run projects, not just a ticket queue.

Direct access to clients and leadership. Your decisions ship the same week.

Constant variety: new systems, new problems, new builds.

Location - Remote (India)

Key Responsibilities 

• Design, develop, and maintain database schemas, stored procedures, views, and queries in SQL Server and MySQL. 

• Identify and resolve query performance bottlenecks using execution plans, indexing strategies, and tuning techniques. 

• Conduct regular database health checks and performance reviews to ensure optimal operation. 

• Collaborate with development and operations teams to support application data requirements. 

• Write and optimize complex SQL scripts to meet business and operational needs. 

• Support the DBA Team Lead with day-to-day database operations and incident resolution. 

• Assist in monitoring database availability, integrity, and backups. 

• Document database structures, procedures, and changes in a clear and organized manner. 

• Implement best practices for data storage, retrieval, and processing efficiency. 

• Contribute to the continuous improvement of database standards and internal processes. 

• Identify and effectively prioritize situations requiring urgent attention. 

• Stay current with system information, database technologies, changes, and updates. 

• Experience with cloud-managed databases (e.g., Microsoft Azure SQL Database, Amazon RDS) and understanding of scaling, cost optimization, and high availability in cloud environments. 

• Hands-on exposure to database automation and CI/CD practices (schema versioning, deployment pipelines, Infrastructure as Code). 

• Strong knowledge of high availability and disaster recovery design, including replication, failover, and defined RPO/RTO ownership. 

• Familiarity with database security best practices (encryption, access control, auditing, and protection of sensitive/regulated data). 

• Experience with monitoring and observability tools, with a proactive approach to performance tuning and alerting. 

• Scripting ability (PowerShell, Python, or Bash) to automate routine database operations and reduce manual effort. 

• Experience with database migrations, version upgrades, or modernization initiatives (on-prem to cloud or legacy to current platforms). 

• Ability to operate in a DevOps-oriented environment, partnering closely with engineering teams and owning database performance tied to application SLA. 

• Proficiency in T-SQL and/or standard SQL. 

• Ability to multi-task and adjust priorities quickly in a fast-paced environment. 

• Ability to research and implement solutions using available technical resources. 

• Strong analytical and problem-solving skills with attention to detail. 

• Ability to clearly communicate technical concepts to non-technical stakeholders. 

• Advanced knowledge of database performance tuning and query optimization. 

• Familiarity with cloud database platforms such as Microsoft Azure SQL, AWS   RDS, or equivalent. 

• Experience with database deployment automation and CI/CD pipelines. 

• Understanding of high availability, disaster recovery, and data protection strategies. 

• Working knowledge of database monitoring and performance management tools. 

• Basic scripting skills (PowerShell, Python, or Bash) for automation. 

• Awareness of data security and compliance considerations in regulated environments. 

Required Skills 

• Proven working experience as a Database Administrator or in a similar role. 

• Working knowledge of Microsoft SQL Server (SSIS, T-SQL, DDL, SML) 

• Working knowledge of MySQL or MariaDB. 

• A minimum of 5 years of experience in database administration. 

• Understanding of database normalization and data modeling principles. 

Nice to Have 

• MacOS, iOS, Android, Linux OS beneficial but not required. 

• ETL processes or data warehouse experience beneficial but not required. 

• Microsoft Certified: Azure Database Administrator Associate or equivalent SQL Server certification preferred but not required. 

• MySQL certification preferred but not required. 

Senior Xano Developer

Visual Backend Development, APIs & AI-Assisted Build

📍 Mumbai (On-site) | Full-time | 3-5 years

About the Role:

Unico Connect is an AI-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies.

We are hiring a Xano Developer who will build backend systems and APIs for our customer engagements on Xano, the visual backend development platform.

The mandatory requirement for this role is hands-on production backend engineering experience, including PostgreSQL data modelling and REST API design, in either a Node.js or Python environment.

Prior production work on Xano is strongly preferred.

The role is hands-on and customer-facing. You will own backend delivery across engagements: designing data models, building API flows in Xano, integrating third-party services, and partnering with frontend, mobile, and AI engineers on contracts and behaviour.

The work spans all three Xano build modes: no-code function stacks, custom code through Xano’s scripting and code blocks, and Xano’s AI assistance and agentic features.

A typical week includes a data model review for a new engagement, building a complex API flow in Xano, integrating a third-party webhook, and a customer working session on a new module.

Responsibilities:

Backend Delivery on Xano

Own end-to-end backend implementation on Xano: database schema, API endpoints, function stacks, background tasks, and integrations.

Ship production-ready work that meets customer requirements and engineering quality standards.

Build Across All Three Xano Modes

Use Xano’s no-code function stacks for standard CRUD and business logic.

Drop into custom code (JavaScript, Python through code blocks, expressions) where visual flows would be unwieldy.

Use Xano’s AI assistance and agentic features to accelerate routine build work.

Database Design on PostgreSQL

Own data model decisions for each engagement: table design, relationships, indexes, addons, and query performance.

Make schema choices that hold up as product usage grows and that map cleanly to the API contracts the client needs.

API Design and Integration

Design clean REST API contracts that the frontend, mobile, and third-party consumers can rely on.

Cover authentication, input validation, pagination, error handling, and rate limiting.

Integrate external services (payment gateways, messaging, storage, AI providers) through Xano’s connectors and custom requests.

Product Thinking and Solutioning

Translate fuzzy product asks from customers into concrete backend solutions.

Ask the right questions about edge cases, data lifecycle, multi-tenancy, and access control before building.

Push back on requirements that will cause pain later.

Customer Communication

Work directly with customers in discovery, design reviews, demos, and weekly working sessions.

Explain trade-offs in plain language, present options with clear recommendations, and write tight technical updates.

AI-Assisted Backend Development

Use Xano’s built-in AI features as well as external AI tools (Claude, Cursor, and similar) day to day for schema drafts, function stack scaffolding, query writing, integration setup, and review.

Develop strong instincts for when AI output is usable as-is and when it must be reworked.

Quality, Testing, and Reliability

Test the flows you ship.

Set up sensible error handling, logging, and alerts for the backend services you own.

Participate in incident response when something breaks in production.

Documentation and Handover

Document data models, API contracts, and non-obvious decisions inside Xano and in shared docs so the rest of the team and the customer can pick up the work without you in the room.

Continuous Learning

Track changes to the Xano platform, including new features, performance improvements, and AI capabilities.

Apply them to active engagements where they reduce build effort or improve product outcomes.

Requirements:

Hands-on Production Backend Engineering Experience (Mandatory)

Must have personally shipped backend systems to production for real users, with ownership of API design and data modelling.

POCs, coursework, and internal-only tools do not qualify.

3 to 5 Years of Professional Backend Engineering Experience

In either a Node.js or Python environment.

Candidates with slightly less time but strong demonstrated ownership are welcome to apply.

Strong PostgreSQL Skills

Schema design, indexing, query writing, and migrations on at least one production system.

Able to reason about query performance and design data models that hold up under realistic product usage.

REST API Design and Integration Depth

Comfort designing API contracts that are clean, predictable, and easy for frontend, mobile, and third-party consumers to work with.

Experience integrating external services such as payment gateways, messaging providers, storage, and AI APIs.

Familiarity with at Least One Node.js or Python Backend Stack

Such as Express, NestJS, Fastify, FastAPI, Django, or Flask.

Comfort reading and writing application code outside of visual environments when the situation calls for it.

Product Thinking and Solutioning

Ability to take a fuzzy product brief, ask the right questions, and propose a backend design that is fit for purpose.

Strong instincts for what to build first, what to defer, and what not to build.

Strong Written and Spoken English Communication

Confident in customer working sessions and design reviews.

Comfortable writing precise technical documentation and explaining trade-offs to non-engineering stakeholders.

Cloud and Deployment Fundamentals

Working knowledge of at least one of AWS, GCP, or Azure: deploying services, reading logs, managing environments, and basic operational tasks.

Familiarity with Docker is a plus.

Bachelor’s Degree

Bachelor’s degree in Computer Science, Information Technology, or a related engineering discipline.

Exceptional candidates with demonstrable production experience and strong portfolios may be considered without a formal degree.

Nice to Have

• Prior production experience on Xano, Bubble, Retool, or comparable visual or low-code backend platforms
• Full stack experience with React, Next.js, React Native, or Flutter
• Experience integrating LLM APIs (OpenAI, Anthropic, Google) into backend workflows
• Multi-tenant SaaS product experience
• Prior agency, consulting, or product-engineering experience

Software Developer

We are looking for a skilled Developer to design, develop, test, and maintain web and mobile applications. The candidate should have experience with modern development technologies, APIs, databases, cloud platforms, Git/GitHub, and AI integration. Good problem-solving, teamwork, and communication skills are required.modern full-stack developer, a strong combination is React/Next.js + Node.js/Python + PostgreSQL/MongoDB + Git/GitHub + AWS + AI/API integration.

POSITION OVERVIEW

We are seeking an experienced Senior Data Scientist & Generative AI Specialist on a contractual basis to support a premier Germany-based chemical manufacturing enterprise. In this role, you will lead the end-to-end design, development, and deployment of production-grade GenAI applications, multi-modal LLM workflows, and advanced retrieval platforms tailored to complex industrial and enterprise data ecosystems.

Working closely with cross-functional global teams, you will build robust backend microservices, implement state- of-the-art RAG/GraphRAG architectures, and leverage cloud-native AI infrastructure (Azure, Vector DBs, Knowledge Graphs) to drive operational efficiency and data-driven innovation.

KEY RESPONSIBILITIES

• GenAI & LLM System Engineering: Design, build, and deploy production-grade multi-modal GenAI applications processing text, structured technical documentation, images, and telemetry data.
• Advanced RAG & Graph Architecture: Implement cutting-edge Retrieval-Augmented Generation (RAG) and GraphRAG pipelines using document parsing frameworks, custom embeddings, vector databases, and knowledge graphs to capture complex domain relationships.
• Scalable Backend Development: Architect high-throughput, low-latency microservice APIs using Python, FastAPI, and Flask, leveraging asynchronous programming (asyncio) and strict type validation (Pydantic) for long-running LLM processes.
• Agentic Systems & Azure Ecosystem: Build autonomous agent systems using modern frameworks (MCP, A2A) and orchestrate enterprise workflows across the Microsoft Azure AI ecosystem (Azure AI Foundry, AI Search, Document Intelligence, Databricks).
• Model Optimization & Evaluation: Execute systematic LLM fine-tuning, prompt optimization, and rigorous evaluation frameworks to assess AI output accuracy, reliability, and business impact against industrial requirements.
• Data Layer Management: Architect and maintain enterprise database layers combining SQL (PostgreSQL) for structured transactional data with specialized vector search engines and graph stores.
• Rapid Prototyping: Utilize AI-assisted development tools (Copilot, Claude Code) to accelerate delivery timelines and rapidly build functional UI prototypes for client feedback.

TECHNICAL QUALIFICATIONS

Core Development & Backend:

• Python Mastery: Deep expertise in writing clean, production-ready Python using asynchronous programming (asyncio), strict type-hinting (Pydantic), and automated testing patterns.

• Backend Microservices: Hands-on experience building microservices with FastAPI and Flask structured to handle asynchronous, long-running AI background tasks.

• Database Engineering: Strong command of PostgreSQL, relational schema design, vector indexing, and knowledge graph paradigms.

Machine Learning & AI Infrastructure:

• Model Expertise: Hands-on experience with leading multi-modal LLM architectures (OpenAI, Anthropic, Google) and domain-specific AI workflows.

• Retrieval & Parsing: Proven track record with document extraction frameworks, embedding models, vector search engines, and GraphRAG architectures.

• Cloud Infrastructure: Strong proficiency with Azure AI infrastructure (Foundry, Databricks, AI Search, Document Intelligence).

• Agentic Frameworks: Practical experience with open-source agent protocols (MCP, A2A), parameter-efficient fine-tuning (PEFT/LoRA), and model evaluation methodology.

CONTRACT & REMOTE REQUIREMENTS

• Contract Engagement: Contractual structure tailored to project milestones and deliverables.

• 100% Remote Setup: Fully equipped home office with high-speed, secure internet infrastructure.

• Timezone Overlap: Guaranteed 4-hour daily overlap with Central European Time (CET/CEST - Germany) to ensure smooth collaboration with enterprise stakeholders.

• Communication: Fluent professional English communication skills (written and spoken) for asynchronous and real-time technical coordination. 

Data Engineer

Data Lakehouse & Platform Engineering 

About the Role

We are hiring Data Engineer to own the lifecycle of our enterprise Data Lakehouse platform. We are looking for engineers who think in systems, make platform-level design decisions, and can build and operate a production-grade, multi-source lakehouse from the ground up, covering ingestion through consumption across a complex, multi-cloud source landscape.

You will be the technical authority for a platform that consolidates data from 18+ enterprise products (Costpoint, GovWin, Specpoint, Vantagepoint, and others) into a governed, medallion-architected data lake on AWS S3 with Apache Iceberg table format, orchestrated via AWS Step Functions, and queryable through AWS Athena and Trino. This role is end-to-end: you own ingestion, transformation, quality, orchestration, ML data supply, and BI consumption.

Key Responsibilities

•      Architect and evolve the full medallion lakehouse — Bronze, Silver, and Gold layers — on AWS S3 with Apache Iceberg; own schema design, partitioning, compaction, and retention policies.

•      Design and implement scalable Glue ETL (PySpark) pipelines for bronze_to_silver and silver_to_gold transformations, incorporating dbt for SQL-layer transformations where appropriate.

•      Own and extend CDC ingestion via Fivetran; manage schema evolution, connector health, and sync reliability across 18+ source products.

•      Build and maintain AWS Step Functions state machines and EventBridge schedules for end-to-end pipeline orchestration; implement Lambda-based quality and drift monitors.

•      Govern the Glue Catalog and Lake Formation policies; enforce column-level security, row-level access controls, and audit logging to meet SOC2 and regulatory requirements.

•      Architect the query layer — optimize Athena workgroups and partition pruning; plan and execute Trino-on-EKS deployment for sub-second analytics workloads.

•      Partner with data science teams on SageMaker data supply: feature engineering pipelines, training dataset preparation, and model registry integration.

•      Implement real-time and near-real-time streaming solutions using Kafka or Kinesis where sub-13-minute latency is required.

•      Lead platform modernization initiatives: evaluate emerging formats (Iceberg vs. Delta Lake vs. Hudi), tooling, and cost optimization strategies.

•      Establish and enforce data engineering best practices: code reviews, CI/CD for pipeline code, IaC (Terraform / CloudFormation), and incident response runbooks.

•      Mentor and level up junior and mid-level data engineers; define team standards for pipeline design, testing, and documentation.

Required Qualifications

•      Software or data engineering experience, with at least 4 years in an architect or technical lead capacity designing large-scale cloud data platforms.

•      Deep, hands-on expertise with AWS data services: S3, Glue (PySpark ETL), Athena, Step Functions, Lambda, EventBridge, Lake Formation, SageMaker, and CloudWatch.

•      Production experience with Apache Iceberg (or Delta Lake / Hudi) table formats — compaction, snapshot management, schema evolution, and time travel.

•      Strong PySpark and Python skills; ability to write, review, and optimize distributed data processing jobs at scale.

•      Hands-on experience with CDC-based ingestion platforms (Fivetran, Debezium, or equivalent) across heterogeneous source systems.

•      Proven experience designing and implementing medallion (Bronze/Silver/Gold) or equivalent multi-hop lakehouse architectures.

•      Experience with data pipeline orchestration: AWS Step Functions, Apache Airflow, or equivalent; event-driven pipeline design patterns.

•      Strong SQL skills; experience with Athena, Trino, Presto, or equivalent query engines for large-scale analytical workloads.

•      Familiarity with data governance tooling: catalog management (Glue Catalog, Apache Polaris/Iceberg REST), data lineage, access controls, and audit frameworks.

•      Experience with Infrastructure as Code (Terraform or CloudFormation) for data platform provisioning and drift management.

•      Solid understanding of dimensional modeling, schema design (star/snowflake), and data normalization for BI and analytics workloads.

•      Bachelor's degree in Computer Science, Engineering, or a related field; or equivalent professional experience.

Preferred Qualifications

•      Experience operating Trino or PrestoDB on Kubernetes (EKS); tuning for sub-second query latency and multi-tenant workloads.

•      Familiarity with streaming platforms (Kafka, Kinesis, or Pub/Sub) and real-time lakehouse patterns.

•      Experience with Apache Polaris or other Iceberg REST catalog implementations.

•      Exposure to SageMaker MLOps pipelines, Model Registry, and feature store patterns for ML data supply.

•      Experience with dbt (data build tool) for SQL-layer transformation and documentation in lakehouse environments.

•      Government contracting or ERP domain knowledge (Costpoint, Deltek, Oracle, or similar enterprise platforms) is a strong plus.

•      AWS certifications: Data Engineer Associate, Solutions Architect Professional, or equivalent.

What You Will Build

You will be a founding architect of a strategic, cross-product data platform that serves 18+ enterprise applications and their analytics, ML, and AI workloads. The decisions you make on schema, storage format, query layer, governance, and orchestration will shape the data foundation of the company for years. This is a high-impact, high-ownership role with direct visibility to senior leadership.

AI Engineer

LLMs, Agents & AI Services

📍 Mumbai (On-site) | Full-time | 2-4 years

About the Role:

Unico Connect is an AI-first technology partner that builds custom mobile, web, and AI products for clients across multiple geographies.

AI is core to how we design, deliver, and scale software for our customers.

We are hiring an AI Engineer for a dedicated client engagement building a complex production AI platform, working on the AI capabilities and agentic features at the core of the product.

The mandatory requirement for this role is at least one AI feature personally shipped to production for real users, with operational ownership.

The role suits someone who thinks quickly on solutioning, can take an ambiguous problem to a working prototype in days, and has the discipline to carry it through to production with predictable economics.

You will work alongside the Senior AI Engineer and the wider pod, with ownership of parts of the AI surface area of the product.

Responsibilities:

Solutioning and POCs

Translate ambiguous customer problems into working POCs at speed.

Pick the right model, framework, and architecture, and demonstrate value early before scaling investment.

LLM Application Development

Build AI features and services using LLM APIs from OpenAI, Anthropic, Google, and self-hosted open-weight models (Llama, Qwen, Mistral).

Choose the right model per use case based on cost, latency, capability, and context-window trade-offs.

Agentic System Design

Design and implement agentic workflows using LangGraph, CrewAI, AutoGen, LlamaIndex Agents, or custom orchestration.

Cover tool use, planning, memory, and multi-step reasoning appropriate to the problem.

API and Service Development

Build production AI services and APIs using Python and FastAPI.

Handle streaming responses, async processing, structured outputs, retries, and graceful degradation when models or tools fail.

Retrieval and Tool Integration

Implement RAG pipelines with vector databases (Pinecone, Weaviate, Qdrant, pgvector, Chroma), embeddings, chunking strategies, hybrid search, and reranking.

Integrate external tools, internal APIs, and document sources through tool-calling and MCP-style patterns.

Cost Analysis and Unit Economics

Model the per-request and per-user cost of every AI feature before it ships.

Track token usage, prompt caching, batching, and model-routing strategies.

Drive measurable improvements in unit economics.

Production Hardening

Add observability and tracing (LangSmith, Langfuse, OpenTelemetry), guardrails, content safety checks, prompt injection defences, and fallback behaviour.

Prompt Engineering and Evaluation

Design, test, and iterate prompts with measured outcomes.

Build evaluation harnesses for accuracy, hallucination, latency, and cost.

Run benchmarks across models and prompt variants before locking in a design.

Requirements:

AI Feature Shipped to Production (Mandatory)

Must have personally built and shipped at least one AI feature that runs in production for real users, with operational ownership.

POCs, internal demos, and one-off scripts do not qualify.

2 to 4 Years of Professional Software or AI Engineering Experience

With at least one production AI feature owned end to end.

Strong Python Proficiency and API Development with FastAPI

Comfort with type hints, async, packaging, testing, streaming responses, and authentication.

Production-grade Python, not notebook-only code.

Hands-on Depth Across the LLM and Agent Stack

Working experience with at least two of OpenAI, Anthropic Claude, Google Gemini, or self-hosted open-weight models (vLLM, Ollama, Together, Replicate).

Working familiarity with at least one agent framework (LangGraph, CrewAI, AutoGen, LlamaIndex Agents) or hand-rolled equivalent.

Working knowledge of RAG, embeddings, and vector databases (Pinecone, Weaviate, Qdrant, pgvector, Chroma).

Solutioning Speed and POC Velocity

Demonstrated ability to move from a fuzzy problem to a working prototype in days.

Strong instinct for what to build first, what to defer, and what to throw away.

Cost Discipline for Production AI

Ability to calculate, monitor, and optimise the cost of LLM APIs, tokens, embeddings, vector store usage, and infrastructure.

Treats unit economics as a first-class concern.

AWS Familiarity

Working knowledge of EC2, S3, IAM, and at least one of Bedrock, SageMaker, or equivalent.

Comfortable in a Fast-Moving Environment

Self-directed, comfortable with ambiguity, takes ownership without being asked, and ships under shifting priorities.

Strong Written and Spoken English Communication

Able to explain trade-offs to non-AI engineers, designers, product managers, and clients in plain language.

Nice to Have

• fine-tuning or LoRA, QLoRA, PEFT exposure
• MCP server authoring
• eval framework experience (LangSmith, Promptfoo, Ragas, DeepEval)
• open-source AI contributions
• multi-modal models (vision, audio)

EGNYTE YOUR CAREER. SPARK YOUR PASSION.

Egnyte is a place where we spark opportunities for amazing people. We believe that every role has meaning, and every Egnyter should be respected. With 23,000 customers worldwide and growing, you can make an impact by protecting their valuable data. When joining Egnyte, you’re not just landing a new career; you become part of a team of Egnyters who are doers, thinkers, and collaborators who embrace and live by our values:

Invested Relationships

Fiscal Prudence

Candid Conversations

ABOUT EGNYTE

Egnyte is the secure multi-cloud platform for content security and governance that enables organizations to better protect and collaborate on their most valuable content. Established in 2008, Egnyte has democratized cloud content security for more than 23,000 organizations, helping customers improve data security, maintain compliance, prevent and detect ransomware threats, and boost employee productivity on any app, any cloud, anywhere.

WHAT YOU’LL DO: 

• Fine-tune and train SLMs using Hugging Face, TRL, and adapter methods (LoRA, QLoRA, PEFT)
• Optimize models for inference via quantization, pruning, and knowledge distillation
• Deploy models to edge devices, mobile, and local servers with strict latency targets
• Build end-to-end MLOps pipelines from data ingestion to deployment
• Monitor model accuracy, latency, and hardware utilization in production
• Evaluate model quality using benchmarking frameworks and custom evaluation suites

YOUR QUALIFICATIONS:

• SLM Development & Fine-tuning: Train and fine-tune SLMs using Hugging Face and Knowledge on Adaptors.
• Model Optimization: Apply quantization, pruning, knowledge distillation, and optimization for lightweight, efficient models.
• Edge Deployment: Deploy models to edge devices, mobile, and local servers, etc.
• Pipeline Engineering: Build end-to-end MLOps pipelines — from data ingestion to deployment.
• Performance Monitoring: Track model accuracy, latency, and CPU/GPU usage in production.

Good to have

• Deployment experience on edge or mobile environments
• Knowledge of ONNX export and cross-platform inference
• MLOps tooling — experiment tracking, model registries, CI/CD for ML

EQUAL EMPLOYMENT OPPORTUNITY

At Egnyte, we celebrate our unique differences and thrive on our diversity for our employees, our products, our customers, our investors, and our communities. Our global Egnyte Employee Communities (EECs) support representation and inclusion across our diverse workplace. Egnyters are encouraged to bring their whole selves to work and to appreciate the many differences that collectively make Egnyte a higher-performing company and a great place to be.

Egnyte will not allow any form of retaliation against employees who raise issues of equal employment opportunity. To ensure the workplace is free of artificial barriers, violation of this policy including any improper retaliatory conduct will lead to discipline, up to and including discharge. All employees must cooperate with all investigations conducted pursuant to this policy.

Job Description:

Position: CAD/CAM Developer / CAD Software Developer

Experience: 3–6 years

Employment Type: Full-time

Location: Hybrid/ Remote

Job Summary

We are looking for a skilled CAD/CAM Developer to design, develop, customize, and optimize CAD-related software applications and plugins. The ideal candidate should have strong programming experience in C++, C#, and/or Python, along with hands-on experience in CAD APIs, 2D/3D geometry, computational geometry, mathematical modeling, and algorithm development.

The candidate will work closely with engineering and product teams to develop high-performance CAD solutions, automate design workflows, process 3D geometry and point-cloud data, and improve the accuracy and efficiency of engineering applications.

Key Responsibilities

• Develop and maintain CAD/CAM software applications, plugins, and automation tools.
• Develop solutions using C++, C#, Python OR .NET.
• Work with CAD APIs such as AutoCAD API, Revit API, or equivalent CAD SDKs.
• Develop algorithms for 2D/3D geometry processing and geometric modeling.
• Design and optimize computational geometry algorithms for complex engineering problems.
• Work with 3D models, point-cloud data, mesh data, and spatial information.
• Implement geometric operations including alignment, rotation, transformation, measurement, and shape/geometry extraction.
• Develop tools for CAD model validation, quality checking, and automated workflows.
• Optimize algorithms for performance, accuracy, and scalability.
• Integrate software components and third-party SDKs into CAD applications.
• Participate in code reviews, debugging, testing, and technical documentation.
• Collaborate with cross-functional teams including engineering, product, and technical teams.
• Follow Agile development practices and maintain source code using Git/SVN.

Required Skills

Programming

• C++
• C#
• Python
• .NET Framework / .NET Core
• Object-Oriented Programming
• Data Structures & Algorithms

CAD / Geometry

• CAD/CAM Software Development
• AutoCAD / Revit / Autodesk platforms
• CAD API / SDK development
• 2D/3D Geometry
• Computational Geometry
• Geometric Modeling
• Spatial Computing
• Mathematical Modeling
• Algorithm Development & Optimization

3D / Point Cloud – Preferred

• Point Cloud Processing
• 3D Reconstruction
• Mesh Processing
• Voxelization
• PCL / Open3D
• LAS / LAZ / E57 / PLY formats
• Coordinate Transformation

We want a highly experienced engineer with expertise as follows:

● React Native: very high level of proficiency; this should be a core strength

● JavaScript / TypeScript: very high level of proficiency

● React: high proficiency, with strong experience building customer-facing web

applications

● Mobile application development: very strong understanding of app architecture,

performance, debugging, reliability, and release quality

● Python / backend systems: medium to high proficiency; comfortable contributing to

APIs and backend workflows

● Performance optimization: strong experience improving responsiveness and efficiency

across application and processing layers

● Database design and caching: solid experience designing efficient data access

patterns and improving performance of critical operations

● AWS / S3 / async pipelines: working experience, especially in systems involving

uploads, processing, and media workflows

● Developer tools and local development workflows: strong experience improving

engineering productivity and reducing friction in how teams build, test, and ship

● Swift / native iOS development: nice to have, but not required

● Product thinking / customer empathy: strong ability to understand user needs, work

through ambiguity, and help shape product direction

The ideal candidate has strong experience building polished, production-quality applications in

React Native and React, and is comfortable contributing across backend systems where

needed. This person should be excited to understand customer workflows closely and help

shape product direction, not just execute tickets.

What you’ll do

You will work on high-impact product and platform problems, including:

● Building and improving the core React Native application used by customers in the field

● Developing mobile product experiences that help customers capture, review, and act on

ergonomics risk insights

● Building frontend UI for critical new features in the web application using React

● Improving app performance, responsiveness, reliability, and release quality across

mobile and web

● Improving the speed and reliability of video ingestion and re-encoding into web-playable

formats

● Implementing intelligent caching and data access patterns to speed up critical database

operations

● Improving developer tools and local development workflows across mobile, web, and

backend systems

● Contributing to backend services and APIs that power our mobile and web experiences

● Supporting native iOS integrations where needed

● Working closely with product, customers, and the broader engineering team to turn

real-world operational pain points into shipped product improvements

Responsibilities

● Own major product features end-to-end, from product thinking through implementation

and release

● Build high-quality, performant mobile experiences using React Native

● Build intuitive, reliable web experiences using React

● Contribute to backend services and APIs in Python that support product workflows

● Improve app quality through better testing, debugging, observability, and engineering

workflows

● Identify and resolve bottlenecks in video, processing, storage, and database systems

that affect product performance

● Help define engineering best practices for application quality, release processes, and

developer productivity

● Collaborate cross-functionally with product and customer-facing teams to prioritize the

right problems

● Raise the technical bar through strong code reviews, debugging discipline, and

pragmatic architectural decisions

Must-have qualifications

●8-14 years of professional software engineering experience

● Strong hands-on expertise in React Native

● Strong hands-on expertise in JavaScript/TypeScript

● Strong experience with React

● Experience building and shipping production-quality mobile applications

● Strong understanding of mobile architecture, app lifecycle, performance, and debugging

● Experience building customer-facing web applications

● Experience working with backend APIs and full-stack product development

● Experience improving developer productivity through tools, local development setups,

testing workflows, or internal engineering systems

● Strong product sense and ability to translate ambiguous customer needs into scoped

technical solutions

● Excellent communication skills and a high degree of ownership

Principal Software Engineer

Company Summary :

As the recognized global standard for project-based businesses, Deltek delivers software and information solutions to help organizations achieve their purpose. Our market leadership stems from the work of our diverse employees who are united by a passion for learning, growing and making a difference. At Deltek, we take immense pride in creating a balanced, values-driven environment, where every employee feels included and empowered to do their best work. Our employees put our core values into action daily, creating a one-of-a-kind culture that has been recognized globally. Thanks to our incredible team, Deltek has been named one of America's Best Midsize Employers by Forbes, a Best Place to Work by Glassdoor, a Top Workplace by The Washington Post and a Best Place to Work in Asia by World HRD Congress. www.deltek.com

Position Responsibilities :

About the Role 

We are seeking a highly motivated AI Solutions Engineer to join Deltek’s growing AI Center of Excellence team to design, develop, deploy, and optimize internal Artificial Intelligence and Machine Learning solutions that solve complex business challenges. The ideal candidate combines deep expertise in AI, machine learning, Generative AI, Large Language Models (LLMs), SLMs, software engineering, cloud computing, and MLOps/LLMOps to build scalable, production-grade AI applications. 

The AI Solutions Engineer will collaborate with AI data scientists, architects, and engineering teams to deliver innovative AI-driven solutions while ensuring security, scalability, governance, and operational excellence. This role reports to the Senior AI Solutions Architect. 

Key Responsibilities 

AI & Machine Learning Development 

• Design, build, train, evaluate, and deploy machine learning and deep learning models. 
• Develop Generative AI solutions using Large Language Models (LLMs) such as GPT, Claude, Gemini, Llama, and Mistral. 
• Implement Retrieval-Augmented Generation (RAG), prompt engineering, fine-tuning, and AI agent frameworks. 
• Build NLP, recommendation systems, forecasting, predictive analytics, and intelligent automation solutions. 
• Optimize model performance, scalability, latency, and cost. 

Software Engineering & Solution Development 

• Develop production-grade AI applications using Python and modern software engineering practices. 
• Build APIs, microservices, and AI-powered enterprise applications. 
• Integrate AI services with enterprise systems, business applications, and data platforms. 
• Apply coding standards, automated testing, CI/CD, and version control best practices. 

MLOps & AI Operations 

• Design and implement MLOps pipelines for model development, deployment, monitoring, and lifecycle management. 
• Automate model training, validation, testing, and deployment processes. 
• Monitor model performance, data drift, hallucinations, and operational metrics. 
• Support continuous improvement and reliability of AI platforms. 

Cloud & Platform Engineering 

• Develop AI solutions on Azure, AWS, or Google Cloud platforms. 
• Leverage cloud-native AI services, containerization, Kubernetes, and serverless technologies. 
• Build scalable architectures supporting enterprise AI workloads and real-time inference. 

AI Governance & Security 

• Ensure compliance with Responsible AI, security, privacy, and regulatory requirements. 
• Implement model governance, explainability, bias mitigation, and risk management practices. 
• Maintain standards for secure design, deployment, and operation of AI solutions. 

Required Qualifications 

Education 

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related technical field. 

Experience 

• 5+ years of software engineering or machine learning development experience. 
• 2+ years of hands-on experience developing and deploying Agentic AI, Generative AI or AI/ML solutions in production environments. 

Technical Skills 

Programming & Engineering 

• Strong expertise in Python. 
• Experience with Java, ReactJS, JavaScript, or similar programming languages. 
• Solid understanding of algorithms, data structures, APIs, and software design principles. 

Artificial Intelligence & Machine Learning 

• Machine Learning and Deep Learning concepts and frameworks. 
• Model training, evaluation, optimization, and deployment. 

Generative AI 

• Large Language Models (LLMs) & SLMs 
• Prompt Engineering 
• Retrieval-Augmented Generation (RAG) 
• AI Agents and Agentic Workflows 
• Fine-tuning and model customization 
• Vector embeddings and semantic search 

Frameworks & Tools 

• PyTorch, TensorFlow, Scikit-learn 
• LangChain, LlamaIndex, Semantic Kernel, MCP, A2A and Transformers 
• FastAPI, Flask 

Data & Analytics 

• SQL and NoSQL databases 
• Data pipelines, ETL, and data modeling 
• Experience with AWS, Azure and Google 

MLOps & DevOps 

• MLflow, Kubeflow, Azure ML, SageMaker 
• Docker and Kubernetes 
• Git, GitHub, Azure DevOps, Jenkins 
• CI/CD automation and model monitoring 

Cloud Platforms 

• AWS (preferred) 
• AWS Bedrock or Azure OpenAI Service 
• AWS SageMaker 
• Google Vertex AI 

Preferred Qualifications 

• Experience designing enterprise-scale AI platforms and products.  
• Knowledge of multi-agent architectures and autonomous AI systems.  
• Experience with vector databases such as Pinecone, Snowflake Cortex, Pgvector, Weaviate, Chroma, or Azure AI Search.  
• Understanding of AI governance, compliance, and Responsible AI frameworks.  
• Relevant certifications in Azure AI, AWS Machine Learning, or Google Cloud AI.

Design and implement an event-level media recommendation engine.

Build candidate recall based on event, venue, time phase, GPS range, media status, and audience type.

Implement visual similarity recall using image embeddings such as SigLIP.

Work with vector search engines such as Qdrant.

Design ranking scores based on freshness, geo distance, event phase, image quality, popularity, and user behavior.

Build recommendation logs for impression, click, dwell time, like, save, share, download, and group join.

Help design attribution analysis and feedback loops for continuous recommendation improvement.

Integrate the recommendation service with our Node.js backend and Flutter app.

Build APIs for feed ranking, similar media search, media upsert, and interaction tracking.

Help us move from rule-based ranking to data-driven ranking as usage grows.

Title - Sr Engineering Manager

Location –Remote

EGNYTE YOUR CAREER. SPARK YOUR PASSION.

Egnyte is a place where we spark opportunities for amazing people. We believe that every role has meaning, and every Egnyter should be respected. With 22,000+ customers worldwide and growing, you can make an impact by protecting their valuable data. When joining Egnyte, you’re not just landing a new career, you become part of a team of Egnyters that are doers, thinkers, and collaborators who embrace and live by our values:

• Invested Relationships
• Fiscal Prudence
• Candid Conversations

ABOUT EGNYTE

Egnyte is the secure multi-cloud platform for content security and governance that enables organizations to better protect and collaborate on their most valuable content. Established in 2008, Egnyte has democratized cloud content security for more than 22,000 organizations, helping customers improve data security, maintain compliance, prevent and detect ransomware threats, and boost employee productivity on any app, any cloud, anywhere. For more information, visit www.egnyte.com

The Monetization Infrastructure team is responsible for all the systems that power Egnyte’s back office: billing, customer intake, account lifecycle and many others. This highly crucial function combines strong business attachment with technical complexity due to Egnyte’s scale and strong pace of innovation.

WHAT YOU’LL DO:

• Lead the Monetization Infrastructure engineering group, reporting to the Platform Engineering VP.
• Be hands-on and lead from the front; provide technical inputs and direction to the group, acting as a check and balance on key technical decisions and helping shape technical direction. Participate and contribute to system designs and code reviews.
• Ensure high quality operation of the systems under your responsibility. Drive a culture of ownership and continuous operational improvement.
• Collaborate with key stakeholders, such as Finance, Product Management and other Engineering groups, to implement end-to-end use cases and support high quality of service.
• Champion fluent use of AI tools across the team and drive adoption of advanced AI-assisted software development lifecycle (SDLC) practices.

YOUR QUALIFICATIONS:

• Managed engineering teams of 15+ people in SaaS product companies, including experience leading managers.
• Hands-on: understand and be able to contribute to system designs. Past background as a staff engineer or architect with a track record of releasing widely adopted solutions.
• Past background in Python (mandatory) and Java (desirable).
• Understanding of cloud platforms (GCP, Azure or AWS) and infrastructure as code concepts is highly desirable.
• Experience in leading distributed teams.
• Fluent in applying AI tools across the engineering workflow, with a track record of driving advanced AI-driven SDLC adoption within a team.

BENEFITS:

• Competitive salaries
• Company equity depending on role and level
• Medical insurance and healthcare benefits for you and your family
• Fully paid premiums for life insurance
• Flexible hours and PTO
• Gym reimbursement
• Childcare reimbursement
• Group term life insurance

Equal Employment Opportunity

At Egnyte, we celebrate our unique differences and thrive on our diversity for our employees, our products, our customers, our investors, and our communities. Our global Egnyte Employee Communities (EECs) support representation and inclusion across our diverse workplace. Egnyters are encouraged to bring their whole selves to work and to appreciate the many differences that collectively make Egnyte a higher-performing company and a great place to be. 

Any employees with questions or concerns about equal employment opportunities in the workplace are encouraged to bring these issues to the attention of hrategnyte.com. Egnyte will not allow any form of retaliation against employees who raise issues of equal employment opportunity. If employees feel they have been subjected to any such retaliation, they should contact hrategnyte.com. To ensure the workplace is free of artificial barriers, violation of this policy including any improper retaliatory conduct will lead to discipline, up to and including discharge. All employees must cooperate with all investigations conducted pursuant to this policy. 

Are you a Python-savvy developer with experience & portfolio of working on python, AI, ML projects looking to work on innovative projects? Look no further! SJTech Solutions is seeking an ambitious and talented candidate to join our dynamic team. 

Key responsibilities:

1. Develop and implement AI models using Python, Machine Learning, Data Science, and Deep Learning techniques.

2. Working on deep learning and machine learning algorithms

3. Working on automation scripts

4. Working on supervised and unsupervised learning algorithms

5. Stay up-to-date with the latest advancements in AI technology and trends.

If you are passionate about AI and eager to work and grow in a fast-paced environment, we want to hear from you! Apply now!

Job Title : Data Platform Engineer (SDE 2 / SDE 3)

Experience : 3 to 8 Years (SDE2 : 3 to 5 Years | SDE3 : 5.5 to 8 Years)

Location : Remote (Contract) → Pune (Post Conversion)

Employment Type : Contract-to-Hire (3 Months)

Mandatory Skills :

Java / Python / Go, SQL, Data Engineering, ETL / ELT, Apache Airflow, Apache Spark / Flink, Kafka, AWS/GCP/Azure, Distributed Systems, API Development, Docker, Kubernetes, CI/CD, System Design

Role Overview :

We are looking for a Data Platform Engineer with strong backend engineering expertise to build scalable, high-performance data platforms and distributed systems. The ideal candidate should have hands-on experience in developing production-grade backend applications along with designing and maintaining modern data pipelines.

Key Responsibilities :

• Build and maintain scalable ETL/ELT and data processing pipelines.
• Develop backend services and APIs using Java (preferred), Python, or Go.
• Design batch and real-time data pipelines using Spark, Flink, Kafka, and Airflow.
• Optimize SQL queries, data models, and distributed data systems.
• Work with cloud platforms (AWS, GCP, or Azure) and container technologies (Docker, Kubernetes).
• Implement CI/CD, monitoring, logging, and performance optimization.
• Collaborate with product and engineering teams on scalable system design and architecture.

Required Qualifications :

• SDE 2 : 3 to 5 years of Backend + Data Engineering experience.
• SDE 3 : 5.5 to 8 years of Backend + Data Engineering experience.
• Strong coding skills in Java (preferred), Python, or Go.
• Excellent SQL and data modeling knowledge.
• Hands-on experience with Airflow, Apache Spark / Apache Flink, or similar technologies.
• Experience building scalable backend services and APIs.
• Good understanding of distributed systems, system design. and scalable architecture.
• Experience with cloud platforms (AWS, Azure, or GCP).
• Hands-on experience with Docker and Kubernetes.
• Strong understanding of CI/CD pipelines.
• Excellent problem-solving and debugging skills.

Good to Have :

• Experience with Snowflake, BigQuery, or Redshift.
• Understanding of Kafka, Kinesis, or Pub/Sub
• Performance optimization of large-scale distributed systems
• FinTech or high-scale distributed systems experience.
• Knowledge of data governance, security, and compliance.

Preferred Candidate Profile :

• Strong Backend + Data Engineering experience
• Experience building scalable production systems
• Strong ownership mindset
• Good system design knowledge
• Experience processing millions of events using Kafka/Spark
• Built APIs supporting data infrastructure
• Production engineering experience

Interview Process :

1. Take-home Coding Assignment (48 Hours)
2. Leadership & Strategy Round (1 Hour)
3. Technical Depth – Data Engineering & Performance (1 Hour)
4. Culture & Values Fit (30 Minutes)

Company Description

We are seeking a QA Engineer to join our engineering team and support the validation of AI-assisted software testing. In this role, you will work closely with senior engineers to evaluate AI-generated test scenarios, validate outcomes, identify inconsistencies, and provide structured feedback that improves product quality and AI model performance.

This is an excellent opportunity for professionals who are detail-oriented, analytical, and interested in working at the intersection of Quality Assurance and Artificial Intelligence.

Job Description

• Execute AI-generated test scenarios against historical job packages and validate expected outcomes.
• Perform manual verification of AI-generated results to determine whether test scenarios have passed or failed.
• Review AI-generated evidence and identify false positives, false negatives, inconsistencies, or missing validations.
• Document defects, observations, and actionable feedback for engineering and product development teams.
• Collaborate with senior QA engineers and software developers to improve product quality and AI testing capabilities.
• Re-test resolved issues and verify fixes as new builds become available.
• Maintain detailed testing documentation and ensure traceability of validation results.
• Participate in QA reviews and continuously contribute to improving AI-assisted testing processes.

Qualifications

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related discipline.
• 5–8 years of experience in Software Quality Assurance, Manual Testing, or QA Analysis.
• Strong attention to detail with excellent analytical and problem-solving skills.
• Ability to interpret test results and identify discrepancies.
• Experience documenting defects and working with engineering teams to resolve issues.
• Good written and verbal communication skills.
• Familiarity with software testing methodologies and defect lifecycle management.
• Exposure to Agile development environments is preferred.
• Experience with AI-assisted testing or automation tools is an added advantage but not mandatory.

Build backend systems that make complex workflows reliable, maintainable, and easier to evolve. You’ll work on production Python services, modernize existing applications, and deliver APIs and integrations used by real teams.

The role is focused on backend development using Python, Django, and FastAPI, with occasional contributions to frontend applications built with React or Next.js. You will work across API development, system integrations, data workflows, background processing, and the modernization of existing services.

You will help improve reliability and operational maturity using AWS, practical Site Reliability Engineering principles, and observability platforms such as Datadog, Grafana, CloudWatch, Prometheus, or similar tools.

This is a backend-first role for an engineer who understands that testing, security, reliability, and observability are core parts of production software development.

What you'll do

• Own the delivery and day-to-day reliability of clearly scoped backend features and APIs built with Python, Django, and FastAPI.
• Improve the maintainability of existing services by simplifying legacy workflows, business logic, data models, and integrations.
• Deliver secure, well-documented APIs that enable frontend applications, internal tools, and third-party integrations.
• Keep data workflows reliable through safe schema changes, migrations, backfills, validation, and failure recovery.
• Build background jobs and asynchronous workflows that handle retries, timeouts, rate limits, idempotency, and operational failures.
• Raise confidence in releases through automated tests, thoughtful code reviews, and production-readiness checks.
• Improve performance and reliability using profiling, query optimization, caching, monitoring, and practical fault handling.
• Make services easier to operate by maintaining useful logs, metrics, dashboards, alerts, and runbooks.
• Contribute to React or Next.js integrations and internal administration interfaces when needed to deliver complete product outcomes.
• Collaborate with product, frontend, platform, DevOps, SRE, and operations teammates to ship well-scoped solutions and document technical decisions.

What we're looking for

Required

• 3–5 years of professional software engineering experience, mainly in backend development.
• Good proficiency in Python and production experience with either Django or FastAPI, with willingness to learn the other.
• Practical experience designing REST APIs and integrating with third-party or internal services.
• Working knowledge of SQL, relational databases, an ORM such as Django ORM or SQLAlchemy, and database migrations.
• Experience writing automated tests and diagnosing defects or production issues.
• Good problem-solving, communication, documentation, and collaboration skills.

Preferred

• Familiarity with background jobs or asynchronous processing using Celery, SQS, RabbitMQ, Kafka, or similar tools.
• Experience deploying or supporting applications on AWS or another cloud platform.
• Familiarity with structured logging, metrics, dashboards, alerts, and observability tools.
• Exposure to React or Next.js integrations.
• Experience with Docker, Redis, webhooks, queues, or event-driven systems.
• Familiarity with CI/CD pipelines, infrastructure-as-code, secrets management, and least-privilege access.
• Experience helping with production incidents, performance testing, or safe deployment practices.

Forward-deployed engineers (FDEs) are Mactores' services layer. You embed with the customer's team, own outcomes from discovery through the production cutover, and personally carry the delivery commitment.

The agent platform we deploy absorbs 60–70% of engagement work, discovery, assessment, design, and testing. You absorb the judgment: target architecture, refactoring trade-offs, model selection, cutover strategy, and the decisions an agent platform cannot make. The agent absorbs scale. You absorb judgment. 

This is not a staff-augmentation seat and not an advisory role. You ship.

What you will do?

• Deliver production agentic AI systems and AWS modernization engagements on committed dates across three pillars: Data Platform Modernization, Application & Database Modernization, and AI Agents for Apps.
• Build and productionize AI agents, orchestration, retrieval pipelines, evaluation harnesses, observability running against real customer data, not demo data.
• Convert existing products into agents: expose product functionality as callable tools for agent-to-agent composition, or replace form-and-click UX with agent-native, intent-driven interfaces.
• Convert existing Business processes into agents: expose process functionality as callable tools for agent-to-agent composition, or replace form-and-click UX with agent-native, intent-driven interfaces.
• Embed directly with customer engineering teams. Run architecture sessions, defend design decisions, and align stakeholders from VP Engineering to CTO.
• Make agent decisions traceable and defensible, validation runs in parallel with live workloads, and outputs hold up to internal audit and regulators (HIPAA, PCI-DSS, FSI-grade governance where the vertical demands it).
• Feed field experience back into the platform and practice: your deployment patterns, integration playbooks, and edge cases shape how we deliver.

What are we looking for?

• Excellent communication skills (English) — verbal and written. Non-negotiable. You will present architecture to customer CTOs, write documents that hold up in audit, and defend judgment calls in the room. If you can build but not explain, this role is not a fit.
• You have shipped production agentic AI systems on AWS. Not POCs, not notebooks — systems running in production for real users. This is the primary qualification. Be prepared to walk through what you shipped, the decisions you made, and what broke.
• Deep understanding of agentic architecture — you can design an agent system from first principles and explain why each component exists:
• Agent design patterns: single-agent vs. multi-agent systems, supervisor/orchestrator patterns, hierarchical agent topologies, planner–executor separation, and when each applies.
• Orchestration: building and operating orchestrator agents that decompose tasks, route work to specialist agents or tools, and manage state across multi-step workflows (LangGraph, Strands Agents, CrewAI, or equivalent).
• Memory: short-term/working memory (context management, conversation state) and long-term memory (episodic and semantic stores, vector- and graph-backed retrieval), and the production trade-offs of each.
• Reflection and self-correction: critique loops, self-evaluation, retry-with-feedback patterns, and evaluation harnesses that catch agent failures before customers do.
• Tool use and function calling: schema design, tool-selection reliability, error handling, and agent-to-agent composition.
• RAG and retrieval pipelines: chunking, embedding, hybrid retrieval, reranking, and grounding agent decisions in customer data.
• Strong AWS production experience: Amazon Bedrock and AWS AI services, plus core platform services (Lambda, API Gateway, DynamoDB, RDS/Aurora, Glue, EMR, Redshift, Kinesis, or similar depending on specialization).
• Solid software engineering fundamentals Python, TypeScript, CI/CD, infrastructure-as-code, testing-driven development discipline.
• Experience with data or application modernization (database migration, legacy refactoring, data platform builds) is a strong plus, since agents run against these workloads.
• Indicative experience: roughly 3–10 years in engineering roles, with agentic AI / GenAI as your current day job. We have demonstrated agent-native expertise over tenure — an engineer with 3–4 years of hands-on agentic AI work typically outperforms a 12-year generalist on this work.

You'll be preferred if you've:

• US English verbal and written fluency 
• Delivery experience in one or more of our verticals: Financial Services, Healthcare & Life Sciences, Internet & Software, Manufacturing, or Telco/Media/Entertainment/Gaming/Sports.
• Model tuning and fine-tuning: systematic prompt engineering and optimization; parameter-efficient fine-tuning (LoRA/QLoRA or similar); instruction tuning; working knowledge of RLHF/DPO; sound judgment on when to fine-tune vs. prompt vs. RAG; and evaluation of tuned models against baselines. Fine-tuning experience on Amazon Bedrock or SageMaker is a plus.
• Experience with compliance-sensitive AI systems (HIPAA, PCI-DSS, SOC 2, data residency).
• Knowledge graph, code-analysis (AST), or CDC/streaming experience (Debezium, Kafka/MSK).
• Solid software engineering fundamentals — Java, C++, Go Lang, .Net, Rust
• Prior customer-facing consulting or forward-deployed experience.
• AWS certifications (Solutions Architect Professional, Machine Learning Specialty, or Data Analytics).

Why This Role?

• You own outcomes, not tickets. FDEs carry the delivery commitment personally — architecture, judgment, and cutover are yours.
• You work agent-native from day one. Our delivery model would not function without agents. You build with the platform, not around it.
• You ship. Engagements measured in weeks to production, legacy retired, outcomes named. No archived pilots.
• You compound. Field delivery informs the Aedeon platform roadmap; the platform's growth expands what you can deliver. Few engineering roles sit in that loop.

Job Description Back-End Developer, Portfolio Management System Dedicated Engagement — Hurricane Capital Role Snapshot Engagement: Dedicated resource, employed/contracted through Transient.AI, invoiced to Hurricane Capital. 100% committed to Hurricane's work — not shared across accounts. Level: Mid-Level back-end developer (not junior, not principal). Location: Remote. Onshore or offshore, but must work U.S. East Coast hours. Type: Full-time, dedicated. Start: As soon as possible. MVP target: end of year. Works with: Rob and Rehan (Hurricane), plus Transient-side engineering for code review. The Work Hurricane Capital is building a portfolio management system from the ground up. This is fully Greenfield — the infrastructure does not exist yet, so you are building it, not maintaining someone else's. The immediate focus is Bloomberg connectivity and the Python back-end that market data flows through. You will be a dedicated engineer for Hurricane, working alongside Rob and Rehan in real time. Because the codebase lives on the Transient side and is reviewed before merge, you will also coordinate directly with Transient engineering. What You'll Own ● Build back-end services and APIs in Python from scratch for the portfolio management system. ● Implement and troubleshoot Bloomberg API connectivity for market data ingestion. ● Design and maintain the data layer — Postgres and SQL, with Snowflake as the cloud data warehouse. ● Work with Azure cloud infrastructure as the system requires. ● Collaborate live over Teams during East Coast hours to co-work and troubleshoot with Rob and Rehan. ● Coordinate with Transient engineering on code review and merges into the shared codebase. What Good Looks Like ● A working MVP of the portfolio management system is delivered by end of year, built in parallel with existing workstreams. ● Bloomberg connectivity is completed and stable, with market data flowing reliably through the Python APIs. ● Hurricane's work moves in real time — no waiting on cross-account turnaround, because this capacity is dedicated. ● Code clears Transient-side review cleanly, with trust established during hiring so there is no gating delay. Must Have ● Strong Python for back-end and API development. ● Hands-on Bloomberg API experience. ● Ability to work U.S. East Coast hours consistently (non-negotiable — daily live collaboration depends on it). ● Database experience: SQL and Postgres. Strong Plus ● Snowflake (cloud data warehouse). ● Azure cloud experience. ● JavaScript. ● Familiarity with capital markets or market data products. Nice for Later The MVP does not require AI/ML work. Over time the system is expected to move in that direction, so appetite to learn and grow into AI-enabled features is a genuine plus — and part of what makes this role interesting. Why This Role ● Completely Greenfield — you build the entire infrastructure from the ground up, not patch an existing system. ● Dedicated to one team, working directly with Rob and Rehan rather than split across clients. ● A clear path to grow into AI-enabled portfolio management as the system matures.

Experience: 8+ years, senior candidates only | Type: Full-time | Location: Remote (India)

 ---

 WHAT WE'RE BUILDING

 See http://www.juliet.space

 We're building Juliet, an AI that runs marketing end to end. Our users are marketers, founders, CEOs, growth leads, agencies, and SMBs — not developers. They

 tell Juliet the goal. She plans, writes production code, and ships real marketing: conversion-optimized websites, launch assets, campaigns, audits, autonomously.

 That's the engineering problem in one line: the humans in the loop can't read code, so the agent has to get it right on her own — plan, build, self-correct,

 recover, ship.

 Under the hood: a browser-based studio backed by cloud sandboxes, a real-time SSE streaming pipeline, and a LangGraph agent working across 83 tools and 63 skill

 modules. The agent isn't bolted onto the product. She is the product.

 Small team, big ambitions. You'll ship things users touch daily, not write tickets about them.

 ---

 THE ROLE

 We're hiring one architect-level backend engineer to own Juliet's agentic infrastructure end to end. That means the agent graph, the execution environment, the

 streaming pipeline, the state and memory systems — and setting technical direction for the engineers working alongside you.

 This is a player-coach seat. You'll still write code every day, and your architectural calls become the product. You'll work directly with the founder. No PMs in

 between.

 Frontend is part of the system. You won't be leading it, but you'll need to understand how the agent's output reaches the browser and be able to ship full-stack

 features when needed.

 ---

 THE STACK

 AI agent (primary): Python 3.11, LangGraph 1.x + LangChain, Anthropic / Google / OpenAI model providers

 API (primary): NestJS 11, Supabase, Redis, PostgreSQL, Server-Sent Events

 Infra (primary): Modal cloud sandboxes, Docker, Netlify deployments

 Frontend (secondary): Next.js 15, React 19, TypeScript, Zustand, CodeMirror 6, XTerm.js

 Monorepo: Turborepo, pnpm

 ---

 WHAT YOU'LL WORK ON

 The majority of your time is here:

 Agentic AI workflows — Design, extend, and harden the LangGraph agent graph: multi-step planning, code generation, tool dispatch, self-correction, and recovery

 across 83 tools and 63 skill modules. This is the core of the product.

 Real-time streaming architecture — The SSE pipeline that carries every agent action from the Python backend through NestJS to the browser: event framing,

 reconnection, health monitoring, interrupt handling for plan approvals and clarifying questions.

 Agent execution environments — Sandbox lifecycle on Modal: container spin-up, file sync, terminal I/O, command execution, and live preview with per-asset esbuild

 bundling. The agent lives here.

 State and memory systems — LangGraph Postgres checkpointers, middleware-injected context (goals, design docs, memory anchors), conversation summarization. How

 the agent knows what it knows.

 Backend API and data layer — NestJS services, Supabase schema, Redis caching, quota enforcement, webhook handling. The plumbing the agent depends on.

 Marketing intelligence pipelines — AEO, CRO, and brand-perception audit engines: multi-LLM probing, parallel inference, streamed structured reports, result

 caching. Audit-at-scale infrastructure.

 The remaining ~25% of your time:

 Full-stack product features — Collaboration (roles and permissions), the Netlify deployment pipeline, subscription and quota flows, onboarding. You'll ship these

 end to end — backend first, frontend to close the loop.

 ---

 WHAT WE'RE LOOKING FOR

 Must-have:

 - 8+ years of professional software engineering, including meaningful time as a tech lead or systems architect who owned something end to end. Closer to ten is

 the norm for people who thrive here.

 - Both worlds on your resume: engineering rigor inside a large company and 0-to-1 ownership at an early-stage startup.

 - Production agentic systems experience. You've built and operated LLM agent systems in production with LangGraph, LangChain, or equivalent — agent graphs, tool

 use, state management, prompt engineering, evals. This means well beyond calling a chat endpoint.

 - Strong Python. You design and ship production Python daily. The agent codebase is yours to own.

 - Architect-level system design. You can own how data flows across four services, make tradeoffs under uncertainty, and defend every call.

 - AI-native development workflow. You drive Claude Code, Codex, or similar agentic tools as everyday instruments — not occasionally. You have opinions about

 working with coding agents because you do it constantly.

 - Real-time backend systems. You've built SSE, WebSocket, or streaming API infrastructure in production — not just consumed it.

 - Strong TypeScript. The API layer and most product features are in TypeScript. You're productive in it.

 Strong plus:

 - Background in developer tools, IDEs, or coding/execution platforms

 - Container runtimes and sandboxed execution (Modal, E2B, Firecracker, or similar)

 - Depth in PostgreSQL, Redis, and Supabase

 - LLM observability and evals tooling (LangSmith or similar)

 - NestJS or equivalent Node.js API framework experience

 - React/Next.js — enough to ship a full-stack feature without handoff

 - Exposure to marketing, growth, or publisher-facing products

 ---

 WHY THIS ROLE IS DIFFERENT

 You own the architecture. Not a feature factory. Not someone else's design doc. The technical execution of an AI product is yours to lead.

 The agent is the product. You're not adding AI to an existing system. You're building and operating the system that is the AI. Every architectural decision

 touches what Juliet can and can't do.

 Hard problems, always. The system spans cloud sandboxes, streaming infrastructure, multi-step agent graphs, and a full-stack web product — for non-technical

 users who can't course-correct a broken output. The bar is high.

 Small team, real leverage. Your code ships to users the same week. No layers of approval.

 ---

 HOW TO APPLY

 Send us:

 1. A short note on the most complex agentic system you've shipped: what broke, and what you'd redo. A link to something you've built that involves agent graphs, tool use, or autonomous multi-step execution

 2. What is one thing you would improve about Juliet? It could be a feature or a bug.

Basic event feed backend structure

2. Media item status model

3. Redis cache workflow for event feed

4. Basic Fast Lane upload processing flow

5. trigger_type and highlight_triggers backend structure

6. One sample admin-triggered workflow, for example goal or full-time

7. Status transition from provisional_ready to ready / pending_review / blocked

Role: Senior Engineer (Cloud Cost Optimization Engineer – Azure FinOps)

Employment Type: Permanent with VDart Digital

Work Location: Remote

Project Description -

Identify and implement cost optimization opportunities across Azure, Databricks, Snowflake, Power BI, and enterprise data platforms.

Drive resource rightsizing, utilization improvements, and governance initiatives.

Build automation, monitoring, and proactive cost controls.

Implement FinOps best practices, cost allocation models, and optimization guardrails.

Partner with engineering and business teams to deliver sustainable cloud savings.

Develop dashboards, reporting, and executive insights on cloud spend and optimization opportunities.

Agentic AI Automation for all the above use cases

Key Skills

• Cloud Cost Optimization & FinOps

• Azure Cost Management

• Databricks Optimization (Photon, Spot Instances, Auto-Termination)

• Snowflake Warehouse & Storage Optimization

• Power BI Capacity Optimization

• Python / PowerShell Automation

• Terraform / Infrastructure as Code

• Azure DevOps & CI/CD

• Resource Rightsizing & Capacity Planning

• Cost Governance, Azure Policy & RBAC

• Cost Analytics, Reporting & Forecasting

Preferred Experience

• 5+ years of experience in Cloud Engineering, Platform Engineering, FinOps, DevOps, or Infrastructure Optimization.

• Hands-on experience with Azure and enterprise-scale cloud environments.

• Proven track record of delivering measurable cloud cost savings and optimization outcomes.

About ZYSYGY

ZYSYGY is building Germany's zero-fee payment network. Merchants pay zero transaction fees. We route payments directly over SEPA Instant, bypassing Visa, Mastercard, and the entire card network chain. No hardware. No card reader. Just a phone.

We are building the first company to combine a fully software-based merchant POS with a consumer super-app for everyday financial life: payments, transport, utilities, government services, all in one place. Think what UPI did for India. We are doing it for Germany, from the ground up, on banking rails.

Role Overview

You will join the core engineering team as a founding engineer. You will own the backend payment engine end to end: transaction flows, wallet management, SEPA Instant settlement, KYB/KYC integration, recurring mandates, refund logic, and the compliance export layer (DATEV, Kassenbuch, GoBD, fiskaltrust). On mobile, you will contribute to the React Native consumer and merchant apps alongside our mobile engineer.

You will own what you build from development through deployment, monitoring, and production reliability. The architecture decisions you make in the first six months will run in production for years.

What you will do

Design and build the backend payment engine: transaction flows, wallet management, SEPA Instant settlement, offline payment authorization, and recurring mandate billing.

Own KYB/KYC integration and the compliance export layer: DATEV, Kassenbuch, GoBD, and fiskaltrust for KassenSichV.

Build REST APIs for financial-grade reliability and high-throughput transaction processing, including full R-transaction handling and reconciliation pipelines.

Contribute to the React Native consumer and merchant mobile apps alongside our mobile engineer.

Own the full lifecycle of what you build, from development through deployment, monitoring, and continuous improvement.

Integrate AI tooling into your development workflow as a matter of course: code generation, automated testing, and intelligent review.

Who you are

Meaningful hands-on experience building payment systems, fintech infrastructure, or financial APIs.

You have designed a ledger. You understand double-entry bookkeeping, immutable transaction records, and why you never update a financial row.

You have solved the partial failure. Money left one wallet and did not arrive in the other. You know how to detect it, resolve it, and make sure it does not happen again.

You understand idempotency at a design level. You have built systems that survive client retries, duplicate webhooks, and network drops without double-charging anyone.

You have built or reasoned about reconciliation: detecting discrepancies between your internal ledger and an external settlement file, and resolving them reliably at scale.

You understand KYC and KYB state machines, what happens when verification status changes mid-transaction, and what a regulator expects from your audit trail.

You are comfortable with SQL databases and own the full lifecycle of your code from development through deployment.

You can communicate technical decisions clearly to non-technical stakeholders. At this stage of the company, that matters.

You are a continuous learner. Payments infrastructure is a deep domain and you treat it that way.

You are comfortable with ambiguity and early-stage risk. There is no playbook yet. You will help write it.

Strong academic background preferred. Demonstrated engineering ability matters more than institution.

Relocation

Relocation to Munich, Germany, is on the table for the right candidate, including visa support.

Connect with the Founder

You can also connect with me on LinkedIn at www.linkedin.com/in/shabbir-maimoon

Job Title : AI Agent / Agentic Engineer

Experience : 4+ Years

Employment Type : Contract

Role Level : Mid–Senior

Project : Leading South Africa Telecommunications Operator

Department : Data & AI Engineering

Job Overview :

We are looking for an experienced AI Agent / Agentic Engineer to design, develop, and deploy intelligent AI agents that automate business workflows and enhance data-driven decision-making.

The ideal candidate will have hands-on experience building autonomous or semi-autonomous AI systems using modern agent frameworks, integrating enterprise APIs, and deploying production-ready agentic solutions with strong safety and governance practices.

Mandatory Skills :

Python, AI Agents, LangGraph, CrewAI, AutoGen, Semantic Kernel, Multi-Agent Systems, Tool Calling, REST APIs, LLMs, RAG, Prompt Engineering, Agent Guardrails, Azure OpenAI (Preferred).

Key Responsibilities :

• Design and develop AI agents for workflow automation and business analytics.
• Build multi-step reasoning, planning, and tool-calling workflows using modern agent frameworks.
• Integrate AI agents with enterprise APIs, databases, and business applications.
• Implement agent guardrails, permission controls, human-in-the-loop workflows, and monitoring.
• Collaborate with GenAI, API, MLOps, and Data Engineering teams to deliver scalable AI solutions.
• Optimize agent performance, reliability, latency, and operational cost.
• Document AI agent capabilities, limitations, and deployment processes.

Required Skills :

• 4+ years of experience in Software Engineering or AI Engineering.
• Strong programming skills in Python.
• Hands-on experience with AI agent frameworks such as LangGraph, CrewAI, AutoGen, or Semantic Kernel.
• Experience building autonomous or semi-autonomous AI agents with multi-step reasoning and tool calling.
• Experience integrating AI solutions with REST APIs, enterprise systems, and data platforms.
• Understanding of LLMs, RAG concepts, prompt engineering, and AI agent orchestration.
• Knowledge of AI safety, guardrails, monitoring, and human-in-the-loop workflows.
• Experience deploying production-ready AI applications.

Good to Have :

• Experience with Azure OpenAI, Azure AI Foundry, or Azure AI Agent Service.
• Exposure to telecom, analytics, or enterprise AI solutions.
• Knowledge of cloud platforms, MLOps, and AI deployment best practices.

Role: Full Stack GEN AI Engineer

Location: Remote - Bengaluru

Duration: Fulltime With VDart Digital

The role demands a developer who is not just familiar with Large Language Models (LLMs), but is an expert in building autonomous agentic workflows using the modern GenAI stack (LangChain, CrewAI, Vector DBs). Expertise in system design, cloud-native technologies, and CI/CD for AI-driven applications is essential for this high-impact delivery role.

Responsibilities

· Design, develop, and maintain full-stack applications that are scalable, robust, and meet the company's quality standards, with a specific focus on Generative AI integration.

· Agentic Orchestration: Build and deploy sophisticated multi-agent systems and autonomous workflows using frameworks like LangChain, CrewAI, or LangGraph.

· Collaborate effectively with cross-functional teams to define, design, and ship new features that bridge the gap between raw AI power and intuitive user workflows.

· Exhibit strong problem-solving skills with an emphasis on product development and driving architecture choices that enable a world-class user experience.

· Utilize a variety of modern web technologies and frameworks (React.js, Angular, or Vue.js) to build responsive and accessible user interfaces.

· Develop and maintain RESTful APIs and services with optimal performance and scalability, handling streaming AI responses and complex function-calling logic.

· Ensure code quality, organization, and automatization through best practices, including unit tests for prompts, model evaluation pipelines, and automated CI/CD for AI-driven features.

· Implement and optimize RAG (Retrieval-Augmented Generation) pipelines using Vector Databases and advanced retrieval techniques.

· Adapt to emerging technologies and frameworks, specifically new GenAI tools and frontier models, and apply them to operational and business needs.

· Manage individual project priorities, deadlines, and deliverables with minimal supervision.

Skill Requirements

· Excellent oral and written communication skills, with the ability to articulate complex AI and technical ideas to both technical and non-technical audiences.

· Profound knowledge of application development, data structures, networking, operating systems, and DBMS.

· Strong proficiency in backend programming languages for API development such as Python, Java, JavaScript (Node.js), or Go.

· Expertise in front-end technologies and frameworks such as React.js, Angular, or Vue.js.

· GenAI & Agentic Tools: Deep hands-on experience with LangChain, CrewAI, or AutoGen. Ability to manage agent memory, state, and tool-calling.

· In-depth understanding of SQL/NoSQL databases, data modeling, and experience with Vector Databases for RAG implementations.

· Solid grasp of system design, microservices architecture, and cloud-native technologies including Docker, Kubernetes, and GitHub Actions.

· Experience with distributed computing, machine learning frameworks, and tools, with a primary focus on Generative AI.

· Desirable (Good to Have): Experience with Generative or Adaptive UI development, where the interface dynamically adapts or renders components based on LLM outputs and real-time AI reasoning.

· A strong desire to learn and master new technologies and techniques in the rapidly evolving GenAI landscape.

Qualifications

· Bachelor’s degree in Computer Science, Engineering, or a related field.

· A minimum of 3-5 years of experience in full-stack development, with a proven track record of building and deploying Generative AI applications and agents.

· Portfolio of successfully deployed web applications and services, specifically showcasing AI agents, RAG implementations, or complex AI-driven features.