50+ Python Jobs in India

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

Strong AI Engineer / Machine Learning Engineer profiles.

2

Mandatory (Experience 1) – Must have minimum 3+ years of hands-on experience in Data Science, Machine Learning, Applied AI, NLP, Deep Learning, or Generative AI solutions.

3

Mandatory (Experience 2) – Must have strong hands-on experience in Python programming, SQL, data analysis, feature engineering, model development, and production-grade ML applications.

4

Mandatory (Experience 3) – Must have experience working with Machine Learning and Deep Learning frameworks such as PyTorch, TensorFlow, Keras, Scikit-learn, or equivalent.

5

Mandatory (Experience 4) – Must have hands-on experience working on NLP, embeddings, semantic search, text classification, document understanding, recommendation systems, or similar AI/ML use cases.

6

Mandatory (Experience 5) – Must have experience working with Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, Phi, or similar foundation models.

7

Mandatory (Experience 6) – Must have hands-on experience building or implementing RAG (Retrieval Augmented Generation) systems, vector search, knowledge retrieval, embeddings, chunking, indexing, or semantic retrieval solutions.

8

Mandatory (Experience 7) – Must have experience working with Git, CI/CD practices, production environments, and scalable AI/ML systems.

9

Mandatory (CTC) – The CTC breakup offered will be 75% fixed + 25% variable, as per company policy.

10

Mandatory (Age) - Candidate's Age should be below 28 Years

11

Preferred (Experience 1) – Experience with MLFlow, Kubeflow, Airflow, Prefect, Feature Stores, Model Registry, or MLOps/LLMOps frameworks.

12

Preferred (Experience 2) – Experience working with Vector Databases, Spark, PySpark, distributed ML pipelines, large-scale data processing, or real-time ML systems..

13

Preferred (Experience 3) – Familiarity with Docker, Kubernetes, Azure, AWS, GCP, cloud-native AI deployments, and scalable ML architecture.

14

Preferred (Company) – Candidates from AI-first startups, Fintech, Banking, Lending, Fraud Analytics, Risk Analytics, Product Companies, SaaS organizations, or data-driven technology companies

15

Mandatory ( Pedigree) - B.TECH / M.TECH from Tier 1 Colleges (IIT's, NIT's, BITS) are Considered.

What You'll Do

• Design, build, and maintain web and backend systems using Python and Node.js
• Develop custom workflows and automation solutions that streamline business processes
• Conduct code reviews, troubleshoot and resolve bugs, and manage database performance and integrity
• Partner with cross-functional teams to gather requirements and deliver effective solutions
• Write clean, maintainable, well-documented code that meets high quality standards
• Mentor and support junior developers through guidance and knowledge sharing

What We're Looking For

• 2–5 years of professional software development experience
• Strong proficiency in Python, Node.js, and REST API design and development
• Hands-on experience with workflow and automation tools
• Self-motivated with excellent communication skills and a collaborative mindset

Why You'll Love This Role

• Take full ownership of projects from concept through delivery
• Grow as a leader by mentoring and developing junior engineers
• Work with direct access to stakeholders and leadership, with real influence on decisions

As a Senior Machine Learning Engineer at Quantiphi, you will be responsible for designing and developing advanced machine learning models and algorithms to solve complex business problems. You will work on optimizing and deploying these models on AWS infrastructure, ensuring scalability and reliability.

Must have skills:

● Experience with LangChain, LangGraph, LlamaIndex, CrewAI, or similar Agentic AI frameworks.

● Python: Good exposure to Python (Pandas, NumPy, FastAPI, advanced Python concepts).

● AWS Bedrock: Hands-on experience with AWS Bedrock and foundation models such as Claude Haiku and Claude Sonnet.

● LLM & GenAI: Hands-on experience in developing RAG pipelines, Prompt Engineering, and LLM-based GenAI applications.

● ML Pipeline Architecture: Experience with Titan Embeddings, Amazon OpenSearch Vector Search, and vector-based retrieval.

● Agentic AI: Hands-on experience with Agentic AI frameworks and AWS Bedrock AgentCore for developing AI agents and workflow orchestration.

● AI Agents & Knowledge Base: Experience developing AI agents for customer query automation and Knowledge Base (KB) solutions.

● Experience with document parsing, chunking, vectorizing and re-ranking strategies.

● Guardrails & Security: Experience configuring AWS Bedrock Guardrails and implementing authentication and authorization for secure AI applications.

● AWS Services: Hands-on experience with API Gateway, Lambda, S3, IAM, CloudWatch, ECR, and SageMaker.

● Software Engineering: Experience with Git, REST APIs, and CI/CD pipelines.

● Relevant AWS certifications (e.g., AWS Certified AI Practitioner, AWS Certified Machine Learning Engineer – Associate, or AWS Certified Solutions Architect – Associate) are a plus.

Good to have skills:

● Hands-on experience with OCR/Document Intelligence engines and NLP techniques for extracting structured information from documents and images.

● Hands-on experience with Bedrock Agentcore

● Hands-on experience with OpenAI, Anthropic, Gemini, or other foundation models.

● Experience with Docker, Kubernetes, and MLOps practices.

● Experience with Redshift, SQL, DynamoDB, or AWS Glue.

● Exposure to model monitoring, evaluation, and observability for GenAI applications.

● Ability to work in an Agile and DevOps environment.

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.

• Articulated English skills, both written and spoken.

• 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.

Strong Software Engineer fullstack profile using NodeJS, Python, and React

2

Mandatory (Experience) - Must have 6+ YOE in Software Development using Python and NodeJS (For backend) & React (For frontend)

3

Mandatory (Core Skills 1): Must have strong experience in working on Typescript

4

Mandatory (Core Skills 2): Must have experience in message based systems like Kafka, RabbitMq, Redis

5

Mandatory (Core Skills 3): Databases - PostgreSQL & NoSQL databases like MongoDB

6

Mandatory (Company) - Product Companies Only

7

Mandatory (Education) - B.Tech or Dual degree (Btech and Mtech or Integrated Msc/MS) from Tier 1 Engineering Institutes (IITs, not NITs). Candidates from other institutions will not be considered unless they come from top-tier product companies

8

Mandatory (Note) : This role is a hybrid role (2 days WFO)

9

Preferred (Experience): Experience in Fin-Tech, Payment, POS and Retail products is highly preferred

10

Preferred (Mentoring): Experience in mentoring, coaching the team.

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.

Forward Deployed Engineer (FDE) – KnackLabs.ai

As a Forward Deployed Engineer at knacklabs.ai, you will sit at the intersection of engineering and customer success. You'll embed directly with customer teams to understand their workflows, rapidly prototype and ship solutions using our platform, and turn those learnings into product improvements. This role is ideal for engineers who want high ownership, fast feedback loops, and direct exposure to how their code changes a customer's business.

What you will own

1. Customer embedding
2. Work directly with customers to understand their business processes, pain points, technical environments, and existing ERP/application landscape.
3. Solution building
4. Design, build, and deploy custom integrations, workflows, and applications on top of the knacklabs.ai platform.
5. Product feedback loop
6. Turn one-off customer builds into reusable, generalizable features by working closely with the core product and engineering teams.
7. Rapid prototyping
8. Write production-quality code under real deadlines, balancing speed with maintainability.
9. Technical troubleshooting
10. Debug issues across the stack, from data pipelines to APIs to front-end integrations, in live customer environments.
11. Ongoing ownership
12. Own the technical relationship with select customers post-deployment, ensuring solutions remain stable and scale with their needs.
13. Travel
14. Travel to customer sites as needed, expected occasionally and varying by account.

What we need from you

1. Three or more years of professional software engineering experience, ideally including customer-facing or implementation work.
2. Strong proficiency in at least one backend language (Python, Node.js, Go, or similar) and comfort working across a full stack.
3. Experience with REST/GraphQL APIs, databases (SQL/NoSQL), and cloud platforms (AWS, GCP, or Azure).
4. Proven experience building and deploying AI-driven solutions within enterprise application ecosystems, including ERP integrations, workflow automation pipelines, and end-to-end product development, with a track record of hands-on stakeholder engagement across technical and business teams.
5. Hands-on experience with ERP systems such as ERPNext/Frappe, SAP, Oracle ERP, Microsoft Dynamics/Navision/Business Central, Odoo, or similar enterprise ERP platforms, including experience with ERP implementation, customization, workflow/module modifications, integrations, or building applications on top of ERP systems, is strongly preferred.
6. Demonstrated ability to work independently in ambiguous, fast-changing environments.
7. Strong communication skills. You can explain technical tradeoffs clearly to both engineers and non-technical stakeholders.
8. A bias toward action. You are comfortable shipping a working solution today rather than a perfect one next month.

About the Role

We are looking for a Forward Deployed Engineer with strong hands-on experience in Databricks and AI-assisted software development using Cursor. The role is suited for an engineer who can work directly with clients and internal teams to understand business problems, rapidly build solutions, and take them from prototype to production.

The ideal candidate should have strong expertise in Python, SQL, Databricks, PySpark, data engineering, APIs, and modern AI-assisted development workflows, along with excellent problem-solving and client-facing skills.

Key Responsibilities

Forward Deployed Engineering

• Work directly with clients and stakeholders to understand business and technical requirements.
• Translate business problems into scalable technical and data solutions.
• Rapidly prototype, test, iterate, and productionize solutions.
• Collaborate with engineering, data, AI, and delivery teams to implement customer solutions.
• Troubleshoot production issues and continuously improve deployed solutions.
• Act as a technical bridge between clients and internal engineering teams.

Databricks & Data Engineering

• Design and develop scalable data solutions using Databricks, PySpark, Python, and SQL.
• Build and optimize ETL/ELT and data processing pipelines.
• Work with Databricks Lakehouse, Delta Lake, Unity Catalog, and Databricks Workflows.
• Develop data ingestion and transformation pipelines for structured and semi-structured data.
• Optimize Databricks workloads for performance, scalability, reliability, and cost.
• Integrate Databricks with databases, APIs, cloud platforms, and enterprise applications.

Cursor & AI-Assisted Development

• Use Cursor and AI-assisted development workflows to accelerate software development, debugging, refactoring, and documentation.
• Effectively use AI coding assistants to understand existing codebases and develop new features.
• Apply appropriate engineering judgment to review, validate, test, and secure AI-generated code.
• Use AI-assisted development for rapid prototyping and proof-of-concept development.
• Work with modern AI/LLM APIs and tools where required for customer solutions.
• Stay current with emerging AI-assisted software engineering practices.

Production & Deployment

• Develop production-ready applications, APIs, and data pipelines.
• Work with Git, CI/CD, APIs, containers, and cloud environments.
• Monitor application and pipeline performance and resolve production issues.
• Ensure solutions meet requirements for scalability, security, reliability, and maintainability.
• Collaborate with Data Scientists and ML Engineers to integrate AI/ML capabilities into production systems.

Required Skills & Experience

• 4+ years of experience in Software Engineering, Data Engineering, AI Engineering, or a related field.
• Strong hands-on experience with Databricks.
• Strong proficiency in Python, PySpark, and SQL.
• Experience with Delta Lake and Lakehouse architecture.
• Experience building production-grade data pipelines.
• Hands-on experience with Cursor or similar AI-powered coding assistants.
• Strong understanding of REST APIs and system integrations.
• Experience working with cloud platforms such as AWS, Azure, or GCP.
• Strong debugging, problem-solving, and analytical skills.
• Excellent communication and client-facing abilities.

Preferred Skills

• Experience with Databricks Unity Catalog, Workflows, and MLflow.
• Experience with Generative AI / LLM applications.
• Knowledge of Claude, OpenAI, Azure OpenAI, or other LLM platforms.
• Experience with RAG, vector databases, embeddings, or AI agents.
• Experience with Docker, Kubernetes, and CI/CD.
• Experience in a consulting, customer-facing engineering, or professional services environment.
• Exposure to Agile/Scrum methodologies.

Key Competencies

• Strong problem-solving and ownership mindset
• Ability to work in ambiguous and fast-paced environments.
• Strong client/stakeholder management skills.
• Ability to understand business requirements and convert them into technical solutions.
• Strong communication and presentation skills.
• Ability to rapidly learn new technologies and tools.
• Comfortable working with AI-assisted development while maintaining high engineering standards.

Education

• Bachelor's or master’s degree in computer science, Information Technology, Engineering, Data Science, or a related discipline.

Role Summary:

We are looking for a Forward Deployed Engineer with strong hands-on experience in Databricks and Generative AI/Claude to work closely with clients, business stakeholders, and internal engineering teams. The ideal candidate will combine strong Data Engineering, Software Engineering, Databricks, and Generative AI skills with the ability to understand business problems and rapidly build, deploy, and optimize production-ready solutions. This is a client-facing, hands-on engineering role where you will work from problem discovery and solution design through POC development, production deployment, and ongoing optimization.

Key Responsibilities:

Forward Deployed Engineering

• Work directly with clients and stakeholders to understand business and technical requirements.
• Translate business problems into scalable data, AI, and software solutions.
• Design and develop POCs and rapidly validate technical solutions.
• Convert successful POCs into reliable, production-ready applications.
• Work closely with client engineering and data teams during implementation and deployment.
• Troubleshoot production issues and continuously optimize deployed solutions.
• Act as a technical bridge between clients, delivery teams, data engineers, AI engineers, and architects.

Databricks & Data Engineering

• Design and develop scalable data solutions using Databricks, PySpark, Python, and SQL.
• Build and optimize data ingestion, transformation, and ETL/ELT pipelines.
• Work with Databricks Lakehouse, Delta Lake, and Unity Catalog.
• Develop Databricks Workflows and production data pipelines.
• Implement data processing solutions for structured and semi-structured datasets.
• Optimize Databricks workloads for performance, scalability, reliability, and cost.
• Integrate Databricks with databases, APIs, cloud platforms, and enterprise applications.

Generative AI & Claude

• Build enterprise AI solutions using Claude and other Large Language Models (LLMs).
• Integrate Claude APIs into applications and business workflows.
• Develop RAG (Retrieval-Augmented Generation) solutions using enterprise data.
• Work with embeddings, vector search, semantic search, and knowledge retrieval.
• Develop AI-powered applications for summarization, classification, information extraction, question answering, and document processing.
• Implement prompt engineering, structured outputs, tool/function calling, and context management.
• Develop and integrate AI agents and multi-step AI workflows where applicable.
• Evaluate LLM responses for accuracy, relevance, groundedness, latency, and cost.
• Implement appropriate AI guardrails, security, and data privacy controls.

Production & Deployment

• Deploy AI and data solutions into production environments.
• Work with APIs, microservices, Git, CI/CD, containers, and cloud platforms.
• Monitor application and pipeline performance and troubleshoot issues.
• Collaborate with Data Scientists and ML Engineers to productionize AI/ML models.
• Ensure solutions meet security, scalability, reliability, and maintainability requirements.

Required Skills & Experience

• 4+ years of experience in Data Engineering, Software Engineering, AI/ML Engineering, or a related field.
• Strong hands-on experience with Databricks.
• Strong proficiency in Python, PySpark, and SQL.
• Experience with Delta Lake and Lakehouse Architecture.
• Experience working with Generative AI / LLMs.
• Hands-on experience with Claude / Anthropic APIs is preferred.
• Experience with RAG, embeddings, vector databases, and semantic search.
• Strong understanding of REST APIs and enterprise integrations.
• Experience developing production-grade applications and data pipelines.
• Strong problem-solving and troubleshooting capabilities.
• Excellent communication and client-facing skills.

Preferred Skills

• Experience with Claude Code / Anthropic ecosystem.
• Experience with OpenAI, Azure OpenAI, AWS Bedrock, or other LLM platforms.
• Experience with LangChain, LangGraph, LlamaIndex, or equivalent frameworks.
• Experience with Databricks Unity Catalog, Workflows, and MLflow.
• Experience with AWS, Azure, or GCP.
• Experience with Docker, Kubernetes, and CI/CD.
• Exposure to AI agents and agentic workflows.
• Knowledge of AI evaluation, guardrails, security, and responsible AI.
• Experience working in consulting, client delivery, or customer-facing engineering environments.

Key Competencies

• Strong customer-facing and stakeholder management skills.
• Ability to understand ambiguous business problems and translate them into technical solutions.
• Strong ownership and execution mindset.
• Ability to rapidly prototype, iterate, and productionize solutions.
• Strong analytical and troubleshooting skills.
• Comfortable working in fast-paced and dynamic client environments.
• Excellent written and verbal communication.
• Ability to work independently as well as collaboratively with distributed teams.

Strong hands-on experience with Adobe Workfront.

Workfront configuration and administration.

Workflows, approval processes, templates, queues, reports, and dashboards.

Custom Forms and Custom Fields.

Strong understanding of Workfront data model and project/work management processes.

Hands-on experience with Workfront Fusion.

Experience with REST APIs, webhooks, JSON, HTTP methods, and integrations.

Experience integrating Workfront with third-party enterprise applications.

Knowledge of automation and iPaaS concepts.

Good understanding of data mapping, transformations, error handling, and integration monitoring.

Knowledge of JavaScript or scripting is an advantage.

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. 

About the company

The client is a quantitative investment firm focused on Indian financial markets. They operate a multi-strategy, multi-manager platform designed to generate consistent, risk- adjusted returns.

Their approach combines systematic investment methods, rigorous quantitative research and institutional-grade manager evaluation. We bring together research, technology and data to build scalable investment solutions.

Role Overview

We are seeking a Quantitative Developer with strong C++ and Python expertise to convert mathematical models and research prototypes into reliable, high-performance analytical engines.

You will work closely with Quantitative Research, Data Engineering, AI and Product Engineering teams throughout the full model lifecycle—from research handover and production implementation to validation, deployment and ongoing support.

This role is ideal for someone who enjoys working at the intersection of quantitative finance, numerical computing and production software engineering.

Key Responsibilities

Research Production

• Translate mathematical models and Python research prototypes into robust, production-quality C++.
• Develop reusable components for risk analytics, forecasting, portfolio analysis and simulation.
• Build efficient Python interfaces for C++ components using pybind11 or similar technologies.
• Ensure production implementations remain mathematically and numerically consistent with the underlying research.
• Establish clear and reproducible processes for transitioning models from research to production.

Engine Development and Validation

• Design and develop analytical engines capable of processing historical, batch and streaming data.
• Integrate calculation components with data pipelines, APIs, databases and downstream applications.
• Validate production implementations against research prototypes, benchmark datasets and expected results.
• Develop automated numerical, unit, integration, regression and performance tests.
• Identify and resolve numerical stability, precision and edge-case issues.
• Optimize calculation speed, memory usage, concurrency and scalability.
• Profile and benchmark critical components to meet defined performance requirements.

Deployment and Delivery

• Package analytical engines as libraries, services, APIs or containers.
• Support deployment across internal infrastructure and client-controlled environments.
• Configure engines for different datasets, workflows and institutional requirements.
• Assist with integration testing, production upgrades, issue diagnosis and technical troubleshooting.
• Implement appropriate logging, monitoring and error-handling capabilities.
• Document interfaces, assumptions, configurations, dependencies and deployment requirements.

Collaboration and Ownership

• Work closely with Quantitative Research, Data Engineering, AI and Product Engineering teams.
• Participate in technical design discussions, code reviews and quantitative model reviews.
• Communicate implementation trade-offs, constraints and risks clearly to technical and quantitative stakeholders.
• Take end-to-end ownership of assigned components, from research handover through production deployment and support.
• Contribute to engineering standards, reusable libraries and development best practices.

Required Qualifications

• Bachelor’s or master’s degree in Computer Science, Engineering, Mathematics, Statistics, Physics, Quantitative Finance or a related discipline.
• Strong professional programming experience in modern C++, including object- oriented and generic programming.
• Proficiency in Python and scientific-computing libraries such as NumPy, pandas or SciPy.
• Experience translating mathematical or analytical prototypes into production software.
• Strong understanding of algorithms, data structures, software architecture and design principles.
• Experience building automated unit, integration and performance tests.
• Familiarity with numerical methods, floating-point behaviour and numerical validation.
• Experience profiling and optimizing compute-intensive or data-intensive applications.
• Proficiency with Git and modern software-development practices.
• Strong analytical, debugging and problem-solving skills.
• •Ability to work effectively with both researchers and software engineers.

Preferred Qualifications

• Experience with pybind11, Boost.Python, Cython or similar interoperability technologies.
• Knowledge of quantitative finance, portfolio analytics, risk modelling, forecasting or simulation.
• Familiarity with time-series data and financial-market datasets.
• Experience developing applications that process batch or real-time streaming data.
• Exposure to concurrent, parallel or distributed computing.
• Experience with containerization and deployment technologies such as Docker.
• Familiarity with Linux environments, CI/CD pipelines and cloud or on-premises infrastructure.
• Experience building analytical libraries, calculation services or APIs for institutional users.
• Knowledge of Indian financial markets is advantageous

Role Overview

We are looking for experienced Azure Databricks Data Engineers with strong hands-on expertise in Apache Spark/PySpark, Python, SQL, and Azure Data Factory (ADF). The candidate will be responsible for designing, developing, and optimizing scalable data engineering solutions on the Azure cloud platform.

Mandatory Skills

• Strong hands-on experience with Azure Databricks
• Strong knowledge of Apache Spark and/or PySpark
• Proficiency in Python
• Strong SQL development and query optimization skills
• Hands-on experience with Azure Data Factory (ADF)
• Experience developing and maintaining scalable ETL/ELT data pipelines
• Good understanding of Azure data engineering concepts and cloud-based data platforms

Key Responsibilities

• Design, develop, and maintain data pipelines using Azure Databricks and ADF
• Develop efficient data processing solutions using PySpark/Spark and Python
• Write complex SQL queries for data transformation, validation, and analysis
• Build scalable ETL/ELT workflows for large datasets
• Optimize Spark jobs, Databricks notebooks, and data pipelines for performance
• Integrate data from multiple sources into Azure-based data platforms
• Implement data quality, validation, error handling, and monitoring mechanisms
• Troubleshoot production issues and provide timely resolutions
• Collaborate with data architects, analysts, developers, and business stakeholders
• Follow best practices for code quality, security, performance, and maintainability

Experience Requirements

For 5+ Years

• 5+ years of overall experience in data engineering
• Strong hands-on experience in Azure Databricks, PySpark/Spark, Python, SQL, and ADF
• Experience working on enterprise-scale data pipelines

For 9+ Years

• 9+ years of overall experience in data engineering
• Strong expertise in Azure Databricks and modern Azure data engineering
• Proven experience designing and optimizing large-scale data pipelines
• Ability to lead technical discussions and mentor junior engineers

Preferred Skills

• Azure Data Lake Storage (ADLS)
• Delta Lake
• Databricks Workflows
• Azure DevOps / CI-CD
• Git
• Data warehousing concepts
• Experience with Agile/Scrum methodologies

Data Engineer - Power BI Modelling

Level: Senior to Advanced - 5-15 years

Locations: Mumbai / Bengaluru

Joining an existing data engineering squad, you own Power BI semantic modeling on top of PySpark/SQL pipeline engineering, delivering enterprise-grade reporting and analytics. As an embedded Data Engineer, you turn PySpark pipelines into trusted Power BI models for the business.

Note: Shares a common PySpark/Snowflake base with "Data Engineer - Graphing" - source together, differentiate on semantic-modeling vs. graphing depth at interview.

Key responsibilities

1. Model semantically. Build and maintain Power BI semantic models (DAX, star schemas) for enterprise reporting.
2. Build pipelines. Develop PySpark/Python and SQL pipelines feeding the Snowflake environment.
3. Partner with stakeholders. Translate reporting requirements into performant data models.
4. Ensure data quality. Validate accuracy and performance of models and underlying pipelines.
5. Collaborate. Operate inside the existing squad with no separate delivery lead required.
6. Explore GenAI. Apply GenAI techniques to reporting and data use cases as opportunities arise.

Must-have qualifications

• Python and PySpark
• SQL
• Power BI semantic modeling (DAX, data modeling)

Preferred

• Snowflake; Dataiku
• GenAI exposure

What success looks like – first 6 to 12 Months

• Power BI models adopted for key reporting use cases
• Reliable PySpark/SQL pipelines feeding those models
• Smooth integration into the existing squad

Confidential – Wissen Technology

What you'll do

The Software Engineer will serve as a full stack developer within the AI and Automation team, focusing on building AI agents, chatbots, and automation solutions. The role involves taking ownership and accountability to meet commitments, developing software using programming languages, and designing, executing, and reporting on system and service tests to ensure applications function as required. Additional responsibilities include monitoring, diagnosing, and resolving technology issues, as well as supporting team members by carrying out prescribed design activities using established procedures.

What you'll bring

3+ years of experience in full stack application development using Python (for backend) and JavaScript/Typescript (for frontend).

Strong understanding of Java.

Strong understanding of Machine Learning /MLOps using Python .

Familiarity with Agentic AI frameworks.

Familiarity with Kubernetes (K8S) and Docker.

Experience in developing solutions using Azure storage and compute solutions.

Proven experience working with Azure AI solutions, especially AI Foundry and AI Search.

Experience in developing distributed event-driven applications using brokers such as Apache Kafka, IBM MQ, Solace, or Azure Event Hub.

Proven experience in working with DevOps frameworks, GIT, and CI/CD pipelines.

Experience developing integrations using established Enterprise Application Integration patterns and tools such as Apache Camel.

Required Qualifications

Specify the minimum educational qualifications necessary for this role, including degree type, field of study, and level attained.

Applicants should hold a bachelor’s degree in any discipline and possess practical experience in software development. Degrees in Computer Science, Software Engineering, or Information Technology are particularly advantageous. 

Role Overview

The Senior AI/ Machine Learning Engineer will design, build, and optimise the core intelligence layer that powers Cliply’s video understanding and content analysis platform.

This role blends deep hands-on engineering with architectural ownership. You will work across video, audio, and text modalities, shaping model design while also delivering production-ready ML systems. You will be the technical anchor for Cliply’s AI stack, partnering closely with the Lead Architect and the engineering team to bring research concepts into scalable, real-world systems.

Key Responsibilities

Multimodal & Video ML Architecture

• Design and validate deep learning architectures for video, audio, and text understanding, including temporal modelling and multimodal fusion.
• Define approaches for long-sequence modelling, representation learning, and sequence-to-sequence tasks.
• Lead experiments with transformers, vision transformers, video encoders, and hybrid multimodal architectures.

Model Development & Optimisation

• Build and optimise models for content understanding, highlight detection, ranking, and scoring.
• Implement training pipelines, data loaders, augmentations, and evaluation metrics for large-scale video datasets.
• Optimize models for latency, throughput, and GPU efficiency using techniques such as quantization, pruning, distillation, batching, and ONNX/TensorRT.

Production ML Engineering

• Convert prototypes into robust, production-ready services.
• Collaborate with backend engineers to deploy models via scalable APIs and micro-services.
• Monitor model performance in production and design retraining loops for continuous improvement.

Technical Leadership

• Establish best practices for experimentation, evaluation, documentation, and reproducibility.
• Provide mentorship to junior engineers and contribute to Cliply’s long-term AI roadmap.
• Influence architectural decisions across the AI stack to ensure scalability and reliability.

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, or related field (Master’s preferred).
• 5–10+ years of experience as an ML Engineer, Applied Scientist, or similar role.
• Strong proficiency in PyTorch or TensorFlow, with hands-on experience training deep learning models.
• Deep expertise in multimodal video machine learning,
• Expertise in at least two of the following:

1. Video understanding / video ML
2. Computer vision
3. Speech/audio processing
4. Natural language processing
5. Multimodal fusion

• Experience with GPU training, distributed training, and large-scale datasets.
• Strong understanding of model optimization (quantization, pruning, distillation, ONNX/TensorRT).
• Solid software engineering fundamentals (Python, version control, testing, code review).

Preferred Qualifications

• Experience with multimodal architectures (video-text, audio-text, cross-modal transformers).
• Experience with MLOps tooling (MLflow, Weights & Biases).
• Prior work in startup environments or fast-paced product teams.
• Contributions to open-source ML projects or competitive ML experience (e.g., Kaggle).

Why Join Cliply

• Build the core intelligence layer of a next-generation video understanding platform.
• Own end-to-end architecture and model design - your work becomes the product.
• Work with a founder-led team that values technical excellence, autonomy, and speed.
• Shape the future of multimodal AI in a real product used by creators and enterprises.

About the Role

We are looking for a motivated Data Engineer with 2+ years of professional experience to join our team. You will be responsible for designing, developing, and maintaining scalable data pipelines and cloud-based data solutions while taking ownership across the full software development lifecycle.

The ideal candidate will have strong experience in modern data engineering practices, cloud platforms, and marketing/advertising data integrations such as Google Ads, Meta Ads, and analytics platforms.

This role is suited for someone who enjoys solving complex data challenges, building reliable systems, and working in a fast-paced environment.

Key Responsibilities

• Design, build, and maintain scalable and reliable ETL/ELT data pipelines.
• Develop and optimize data models, transformations, and warehouse solutions for analytics and reporting.
• Work with marketing and advertising datasets from platforms such as Google Ads, Meta Ads, Google Analytics, and similar ecosystems.
• Integrate APIs, third-party systems, and cloud-native services into data workflows.
• Optimize complex SQL queries and improve pipeline performance and reliability.
• Implement data quality checks, monitoring, and observability across pipelines.
• Collaborate with cross-functional teams including product, analytics, and engineering teams to deliver data-driven solutions.
• Contribute to software engineering best practices including Git workflows, CI/CD pipelines, testing, and documentation.
• Participate in architecture discussions and help improve data platform standards and best practices.
• Build and maintain solutions within Google Cloud Platform (GCP) environments.

Qualifications

• 2+ years of professional experience in Data Engineering or related fields.
• Strong proficiency in Python programming.
• Advanced SQL skills with experience in query optimization and data warehousing concepts.
• Experience working with marketing and advertising platforms such as Google Ads, Meta Ads, Google Analytics, DV360, or similar platforms.
• Experience working with AI-assisted coding and development tools such as Claude Code, Cursor, or similar platforms
• Hands-on experience leveraging AI-based development tools to accelerate data engineering implementation and automation
• Familiarity with AI-powered coding assistants and agentic development workflows
• Understanding of marketing data pipelines, attribution reporting, campaign analytics, or customer analytics is highly desirable.
• Hands-on experience with at least one major data engineering technology such as Airflow, Spark, Kafka, Databricks, or similar frameworks.
• Strong experience with Google Cloud Platform (GCP) services such as BigQuery, Cloud Run, Cloud Functions, GCS, or Composer.
• Familiarity with APIs, Linux environments, CLI tools, Git, and CI/CD workflows.
• Strong understanding of data pipeline design, orchestration, monitoring, and troubleshooting.
• Excellent communication, collaboration, and problem-solving skills.
• Ability to work independently and contribute across multiple technical domains.

Who are we?

Inflexion Analytics is a team of data science and analytics consultants based in London, UK, and Bangalore India. Founded in 2015, we have built a strong track record and foundation serving demanding clients. We are now looking to achieve significant growth and become a leading specialist consultancy in the data science field.

What we do?

We help businesses to improve performance through better insight and decision-making. To achieve this, we offer services across the data science value chain including; data strategy, data engineering, data insights, and visual analytics. We work with a global client base, including clients based in the US, UK, Europe, and Australia. 

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.

About the Role:

We are looking for a hands-on Senior Backend Engineer who can own complex product and platform problems end-to-end. You will work across AI agents, workflow orchestration, enterprise integrations, and distributed systems that operate reliably in real-world IT environments.

This role requires strong engineering judgment, product thinking, and the ability to transform ambiguous requirements into scalable, production-ready systems.

Key Responsibilities

• Build the core platform powering agentic IT operations.
• Design reliable multi-step workflows with retries, approvals, rollback mechanisms, and recovery.
• Develop AI pipelines for intent understanding, planning, validation, and execution.
• Build integrations with Microsoft 365, PSA, RMM, and other IT management platforms.
• Design secure, scalable multi-tenant enterprise systems.
• Improve production observability, monitoring, and reliability.
• Own major features from architecture and development through deployment and production support.
• Mentor engineers and contribute to raising the technical standards of the team.

Required Skills & Experience

• 8+ years of experience building backend platforms, distributed systems, or enterprise applications.
• Strong software engineering fundamentals and system design expertise.
• Experience building REST APIs, asynchronous/event-driven systems, databases, and cloud-native applications.
• Strong problem-solving skills with the ability to independently drive solutions in ambiguous environments.
• Excellent debugging, production engineering, and ownership mindset.
• Comfortable using AI coding assistants while independently validating code quality and correctness.

Preferred Technical Stack

Languages & Frameworks

• TypeScript
• Node.js
• Python

Databases & Messaging

• PostgreSQL
• Redis
• Message Queues
• Event-driven architectures

Cloud & Infrastructure

• AWS
• Docker
• Terraform

Enterprise Integrations

• Microsoft Graph API
• Microsoft 365
• OAuth
• Webhooks
• PSA/RMM platforms

AI & Workflow Technologies

• Workflow engines
• State machines
• LLM Agents
• Retrieval-Augmented Generation (RAG)
• AI evaluation frameworks

What We're Looking For

• Strong backend engineering expertise with experience building scalable production systems.
• Deep understanding of distributed systems and platform architecture.
• Ability to own features from design through production.
• Product mindset with strong engineering ownership.
• Excellent collaboration and mentoring skills.

Why Join Neels AI?

• Build the operating layer for the next generation of IT services.
• Work on AI systems that take real-world actions—not just generate text.
• Solve challenging problems across AI, infrastructure, security, and enterprise automation.
• Join at an early stage and influence product direction, architecture, and engineering culture.
• Work directly with experienced founders and global enterprise customers.

Overview

Seeking for an experienced Automation & Reliability Engineering Lead to design, build, and lead automation and metrics solutions that reduce manual support toil and improve operational visibility across Support and SRE teams. This is a hands-on technical leadership role focused on engineering enablement, not tool-specific automation or day-to-day production support.

Key Responsibilities

* Define automation strategy and reference architectures for Support and SRE workflows

* Lead and mentor a small automation engineering team (may start as an individual contributor role)

* Design and build automation utilities, services, and frameworks to eliminate manual operations

* Engineer data pipelines to extract, normalize, and correlate operational data from systems such as Salesforce, email, logs, and internal services

* Define metric semantics (SLAs, lifecycle events, ownership, timestamps) and ensure data accuracy

* Partner with Support, SRE, and Power Platform teams to enable dashboards and reporting

* Establish standards for reliability, error handling, auditability, and documentation in automation solutions

* Identify, prioritize, and track automation opportunities based on toil reduction and risk mitigation

Required Qualifications

* 8+ years of experience in software engineering, automation, or platform engineering

* Strong hands-on programming experience in Python OR Java (one primary language required)

* Proven experience building internal tools, automation frameworks, or backend services

* Solid understanding of APIs (REST), data formats (JSON, CSV), and system integration

* Experience working with operational or production systems and understanding support workflows

* Ability to translate business or operational needs into reliable engineering solutions

Preferred Qualifications

* Experience designing or contributing to automation programs at scale

* Familiarity with observability concepts such as logging, metrics, and alerting

* Working knowledge of SQL or data persistence technologies

* Prior experience collaborating with SRE, DevOps, or Support teams

Nice to Have

* Exposure to AI/ML or GenAI techniques applied to operational automation (classification, summarization, anomaly detection)

* Experience supporting metric pipelines feeding BI or workflow automation platforms

* Healthcare or SaaS domain experience

Position: Computer Vision Engineer

Experience: 2–3 Years

Location: Bengaluru, Karnataka

Employment Type: Full-time

About the Role

We are seeking a highly motivated Computer Vision Engineer to join our autonomy and avionics team. The role involves developing, implementing, and validating computer vision models and algorithms and pipelines for UAVs operating in both GNSS-available and GNSS-denied environments.

The ideal candidate should have a strong foundation in theory of deep learning and machine learning, strong understanding of electromagnetic spectrum, imaging fundamentals, camera principles, and mathematical concepts with hands-on experience in implementing these algorithms on embedded or real-time systems.

Key Responsibilities

• Design, develop, and optimise AI Models
• Make custom CNNs/ modify existing CNNs to suit specific problems at hand
• Handle end-to-end training flow
• Implement end to end inference pipelines on standard PCs as well as on embedded systems
• Understand performance benchmarks and assess the accuracy and inference times
• Implement traditional image processing algorithms
• Factor the code to leverage underlying hardware architecture
• Prune the networks for efficiency
• Integrate the system within the application framework using C++
• Work closely with perception, controls, embedded software, and systems engineering teams.

Required Qualifications

• B.E./B.Tech/M.E./M.Tech in Computer Science and Engineering, Electronics, ECE, Mechatronics, or a related discipline.
• 2–3 years of experience in relevant area
• Strong understanding of: Linear Algebra, Probability and Statistics, AI-ML-DL fundamentals, Image processing, Camera Functioning
• Strong programming skills in C++ and Python.
• Experience with MATLAB for algorithm development and validation.
• Familiarity with Linux development environments.
• Experience with Git version control.

Preferred Skills

• Experience with Camera, IMU Calibration and Synchronisation
• Experience with multi-sensor fusion.
• Experience working with NVIDIA devices
• Experience on FPGA will be an added advantage
• Full understanding of Git functionality
• Exposure to airborne software development processes and coding standards (e.g., MISRA C++).

Personal Attributes

• Strong analytical and problem-solving skills.
• Ability to work independently on challenging technical problems.
• Good communication and documentation skills.
• Passion for solving challenging problems
• Willingness to participate in field trials and flight testing.
• Team playwe

 Job Description:

• Job title : Senior DevOps Engineer (On-Premises Automation)        
• Experience: 8+ years
• Location: Chennai / Pune / Hyderabad / Bangalore
• Shift: UK Shift
• Work Mode: Onsite (WFO)

Roles and Responsibilities:

• This engagement automates end-to-end infrastructure build provisioning across on-prem and Azure. The team will build a shared ServiceNow-led intake, approval, orchestration, and closed-loop status model, using Jenkins/Ansible for on-prem builds and Azure DevOps/Terraform for cloud builds. Automation includes standards, security/compliance controls, scan gates, CMDB/change updates, and handover.              

Key Responsibilities:

• Lead secure and repeatable on-prem build provisioning pipelines
• Lead Jenkins CI/CD for automated on-prem provisioning and configuration.
• Create pipeline templates for VM/physical host, OS deployment, configuration, certification, and rollback.
• Embed hardened-image validation, scan gates, secrets handling, approvals, and audit evidence.
• Lead Ansible roles, PowerShell modules, test automation, and code reviews.
• Implement logging, monitoring, and root-cause analysis; support UAT and hypercare.       

Required Qualifications:

• 8–10 years in DevOps, platform engineering, or infrastructure automation.
• Bachelor’s degree or equivalent experience.
• Jenkins
• Git, CI/CD
• Ansible, PowerShell
• Python, IaC
• security scanning, secrets management
• Windows/Linux.

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)

Job Summary

We are looking for a skilled and experienced Data Engineer to join our growing data team. The ideal candidate will have strong expertise in Python, PySpark, Data Modeling, and Power BI, with hands-on experience in designing, developing, and optimizing scalable data solutions. The role requires working closely with business stakeholders, data architects, and analytics teams to build robust data pipelines and semantic models that enable data-driven decision-making.

Technical Skills

• Strong hands-on experience in Python and PySpark development.
• Expertise in building and optimizing Data Engineering solutions and ETL pipelines.
• Strong understanding of Data Modeling concepts (Star Schema, Snowflake Schema, Dimensional Modeling).
• Experience with Power BI Data Modeling and Semantic Layer development.
• Proficiency in DAX (Data Analysis Expressions).
• Experience designing and managing Semantic Models in Power BI.
• Strong SQL skills and experience working with large datasets.
• Knowledge of data warehousing concepts and best practices.

Preferred Skills

• Experience with cloud platforms such as Azure, AWS, or GCP.
• Exposure to modern data platforms like Databricks.
• Understanding of data governance and data quality frameworks.

About the Company

The client is revolutionising the way businesses operate through cutting-edge technological solutions. Their focus is on developing intelligent agents and agentic workflows that automate processes and eliminate the need for human effort wherever possible. By leveraging

advanced AI and machine learning, they create systems that enhance productivity and drive efficiency.

Their expertise extends to the fintech, healthcare and medical technology sectors, where they develop innovative solutions that improve patient outcomes and streamline medical operations.

From medical devices to healthcare platforms, their work sits at the intersection of technology and medicine, pushing the boundaries of what's possible. The team is dedicated to continuous learning and growth, ensuring the team members are always at the forefront of the tech landscape.

About the Role

This is a senior, hands-on engineering role at the heart of our product team. You will be one of the most technical people in the room — setting the architecture for our real-time voice AI

agents and building the hardest parts of it yourself. From the systems that power live conversations to the interfaces our clients rely on, you will own how the product is engineered end to end.

We are looking for a genuine lead full-stack engineer with the depth to make architecture decisions that hold up as we scale, and the appetite to still be in the code every day. You should be as comfortable designing the backend services behind a live voice agent as you are shaping a clean interface on top of them — and comfortable being the person others turn to when something is hard.

You will work directly with the founder and product leadership on a fast-moving product, with real influence over technical direction. This is a role for someone who wants ownership at the level of "how the whole thing is built," not just individual features — and who raises the bar for

everyone around them.

What You'll Own

Set the technical direction

• Own the architecture of our core systems — the real-time voice agents, backend
• services, data and APIs — making the decisions that keep the product fast, reliable and scalable as it grows.
• Lead the hardest engineering problems and solve them personally.
• Establish engineering standards — code quality, review practices, testing and technical patterns that the team builds to.
• Drive technical strategy with the founder and product leadership — shaping the roadmap, flagging risk early, and turning product ambition into a sound technical plan.

Build the product end to end

• Design, build and ship features across the stack — backend services, APIs and front-ends — owning them from idea to production.
• Build the client-facing surfaces — dashboards, review tools and configuration interfaces that let our clients run and trust the product.
• Design and evolve the data models and APIs that hold up as we scale across clients.

Make it reliable and fast

• Own production quality — put the monitoring and alerting in place so issues are caught before clients feel them, and performance stays within target.
• Care about performance — find and fix bottlenecks across the stack.
• Build for correctness — put the testing and evaluation in place that keeps the product behaving predictably as it changes.

Lead through the team

• Mentor and grow engineers — through code review, pairing, and setting a technical example others learn from.
• Multiply the team's output — unblock others and lift the overall quality of the codebase.
• Take features from ambiguity to done — turn a rough product goal into a shipped, working capability with minimal hand-holding, and help others do the same.

What We're Looking For

• 8+ years of professional software engineering experience, with significant depth across both backend and frontend and a track record of owning systems, not just features.
• Strong backend engineering, ideally in Python — building and scaling production services and APIs.
• Strong frontend engineering with a modern framework such as React — able to architect and build polished, responsive interfaces independently.
• Proven architecture and system-design ability — you have designed systems that scaled, and can reason clearly about trade-offs.
• Solid fundamentals across APIs, databases and cloud infrastructure.
• Experience building real-time and/or AI-powered products — or clear, demonstrable ability to lead in this area.
• A history of technical leadership — setting standards, mentoring engineers, and being trusted with the hardest problems — while remaining hands-on.
• Excellent communication and a genuine ownership mindset — someone who can be handed an ambiguous, high-stakes problem and be trusted to see it through.

Nice to Have

• Experience working with AI / large language models in production.
• Experience with voice or other real-time products.
• Exposure to healthcare, fintech, or other regulated / high-stakes domains.
• Experience as an early or senior engineer in a startup, where you set direction and wore many hats.

Company Description

Appiness Interactive Pvt. Ltd. is a Bangalore-based product development and UX firm that

specializes in digital services for startups to fortune-500s. We work closely with our clients to

create a comprehensive soul for their brand in the online world, engaged through multiple

platforms of digital media. Our team is young, passionate, and aggressive, not afraid to think

out of the box or tread the un-trodden path in order to deliver the best results for our clients.

We pride ourselves on Practical Creativity where the idea is only as good as the returns it

fetches for our clients.

About the Role

You will own the reliability of the distributed data systems, the streaming runtime and

processing engines that move hundreds of billions of rows per day for top-tier enterprises. This

is an SRE role for our big data stack: Kafka, Spark, Flink, Ray, Redis, and data warehouses, all

running on Kubernetes.

This is not a cloud-provisioning role. We are looking for someone who has lived inside stateful,

high-throughput systems in production who has chased down a broker outage, a checkpoint

stall, a crashlooping cache, and a sink that silently stopped writing, and who fixes the

architecture rather than the symptom. If keeping a large, busy data platform alive and fast is the

kind of problem you find satisfying, you will have a lot of fun working with us. This is a unique

opportunity to shape the foundation of a product that is defining the next wave of intelligent,

context-aware data movement.

Responsibilities

● Streaming & Data Plane Reliability: Own the health of our Kafka-based runtime

(managed via Strimzi on Kubernetes) - broker health, topic lifecycle and count

management, partition and throughput tuning, certificate/secret rotation, and version

upgrades - at a scale of hundreds of thousands of topics and hundreds of billions of rows

per day.

● Distributed Processing Engines: Operate and tune distributed system workloads in

production in collaboration with backend teams, resource allocation, autoscaling,

checkpointing, backpressure, and failure recovery for both batch and streaming jobs.

● Stateful Services: Run Redis clusters and other stateful systems reliably - failover,

persistence, liveness/readiness tuning, and capacity planning under heavy and bursty

load.

● Kubernetes & Operators: Take end-to-end ownership of Amazon EKS, Google GKE and

the operators (Strimzi and others) running our stateful data workloads - cluster lifecycle,

scaling, version upgrades, and resource governance.

● Observability: Build deep, data-aware monitoring - consumer lag, throughput, partition

skew, job latency, error rates - not just host and CPU metrics. Make the data plane's

behavior legible before it breaks.

● Incident Management: Lead root-cause analysis for distributed-systems failures (broker

outages, crashloops, sink decommissions, control-plane race conditions) and drive

durable fixes. Mitigate fast, but design out the recurrence.

● Infrastructure as Code & Automation: Provision and manage cloud infrastructure with

Terraform; build operational runbooks and automation, including for air-gapped/private

enterprise installs (pre-staged images, operator-facing procedures).

● Collaboration: Partner with platform, runtime, and connector engineering - and with

SREs and support - to ship and scale new data-movement features reliably in a

large-scale Linux environment.

Qualifications

● Experience: 6+ years in infrastructure, SRE, or DevOps, with significant time spent

operating production distributed data systems (not just application/cloud infra).

● Kafka: Deep, hands-on operational experience running Kafka at scale in production -

ideally on Kubernetes via Strimzi - including upgrades, topic/partition management,

performance tuning, and TLS/secret rotation.

● Distributed Processing (Strong Plus): Production experience operating one or more of

Spark, Flink, or Ray - resource tuning, checkpointing, failure recovery.

● Stateful Systems (Must Have): Production experience with Redis (clustering, persistence,

failover) and a solid understanding of operating stateful workloads on Kubernetes

(StatefulSets, PVCs, probes, operators).

● Data Warehouses: Familiarity operating against Snowflake, BigQuery, or similar, and an

understanding of JDBC connectivity and sink reliability.

● Kubernetes & EKS: Strong hands-on EKS cluster creation, scaling, version upgrades, and

operator management.

● Infrastructure as Code: Advanced proficiency with Terraform.

● Programming: Proficiency in Python (or similar) for automation and tooling. Comfort

reading and debugging JVM-based systems is a strong plus.

● Reliability Mindset: Demonstrated ownership of incident management, RCA, capacity

planning, and performance tuning for high-throughput systems.

● CI/CD: Solid understanding of CI/CD methodology (Jenkins, GitHub Actions, or GitLab CI)

for containerized and non-containerized apps. Supporting, not the core of the role.

● Nice to Have: Configuration management (Ansible preferred); broader AWS services

(IAM, VPC, EC2, S3, Lambda); AWS CloudFormation.

● Soft Skills: Excellent communication and organizational skills; ability to coordinate

effectively within a team and with customers.

Why This Might Be Worth It

● You own the hard part. The stateful, distributed systems that move billions of rows are

the platform's most demanding reliability problems - and they'd be yours.

● Impact at scale from day one. Your work keeps mission-critical data flowing for

companies like DoorDash and LinkedIn.

● The AI wave is real for us. We're not bolting AI onto a legacy product. Intelligent

connectors, context-aware data movement, and agentic workflows are the core of what

we're building next - on top of the runtime you'd run. ○ Small team, big problems. Direct

access to the CTO, real influence over product direction, and the autonomy to make

significant technical bets. ○ Recognized platform, startup energy. Enterprise validation

with the speed and ownership of an early-stage company.

Role Summary

Seeking an experienced SQL Developer with strong expertise in Data Lake architecture, Data Engineering, AI/ML data modelling, and Vector Database design. The candidate will be responsible for building scalable data platforms, developing optimized SQL solutions, designing AI-ready data models, and supporting enterprise analytics and GenAI initiatives.

Key Responsibilities

• Design, develop, and optimize complex SQL queries, stored procedures, views, and database objects.
• Build, maintain, and govern enterprise Data Lakes for structured, semi-structured, and unstructured data.
• Design scalable data models for Analytics, Machine Learning (ML), and AI applications.
• Develop and maintain data ingestion, transformation, and data preparation pipelines.
• Architect and manage Vector Database solutions supporting GenAI, semantic search, embeddings, and RAG-based applications.
• Ensure data quality, security, performance, governance, and scalability across platforms.
• Integrate data from multiple enterprise systems, databases, APIs, and business applications.
• Collaborate with Business, Analytics, Data Science, and AI teams to deliver enterprise data solutions.

Mandatory Skills

• Advanced SQL Development (SQL Server, PostgreSQL, Oracle, MySQL, etc.)
• Data Lake Architecture, Development, and Maintenance
• Data Warehousing & Dimensional Data Modelling
• AI/ML Data Modelling and Feature Engineering
• Python for Data Engineering, Data Processing, and Automation
• Query Performance Tuning & Database Optimization
• Data Governance & Data Quality Management
• Vector Database Architecture and Management (Pinecone, Qdrant, Weaviate, Milvus, Chroma, or similar)
• Experience handling large-scale structured and unstructured datasets

Preferred Skills

• Experience with GenAI, RAG (Retrieval-Augmented Generation), Embeddings, and LLM-based applications
• PySpark and Distributed Data Processing
• Power BI or Enterprise Reporting Platforms
• Knowledge of MLOps, AI data pipelines, and modern data architectures

Key Attributes

• Strong analytical and problem-solving skills
• Ability to independently own end-to-end data platform solutions
• Excellent communication and stakeholder management skills
• Passion for Data Engineering, AI, ML, and GenAI technologies

Ideal Candidate

A hands-on SQL Developer who can build and maintain enterprise Data Lakes, design AI/ML-ready data models, develop Python-based data solutions, and architect Vector Database platforms to support advanced analytics, AI, and GenAI initiatives.

Job Title: Automation Engineer – Watermelon Tool 

Experience: 6–7 Years

Location: Bangalore

Work Mode: Hybrid

Notice Period: Immediate Joiners (Apply only if you can join within 15 Days)

Key Responsibilities:

Design, develop, and maintain automated test scripts using the Watermelon automation tool.

Build and enhance automation frameworks for web, API, and enterprise applications.

Analyze business and functional requirements to identify automation opportunities.

Execute automated regression, smoke, sanity, and functional test suites.

Maintain reusable automation components and improve test coverage.

Integrate automation scripts with CI/CD pipelines.

Perform root cause analysis for failed test cases and provide detailed defect reports.

Work closely with developers, business analysts, and QA teams to resolve issues.

Ensure automation standards, coding best practices, and documentation are followed.

Participate in Agile ceremonies including sprint planning, stand-ups, reviews, and retrospectives.

Required Skills:

5–10 years of experience in Automation Testing.

Strong hands-on experience with the Watermelon automation tool (mandatory).

Experience in test automation framework development and maintenance.

Knowledge of API testing and automation.

Experience with SQL for database validation.

Familiarity with Git or other version control systems.

Experience with CI/CD tools such as Jenkins, Azure DevOps, or GitLab CI.

Strong understanding of SDLC, STLC, and Agile methodologies.

Excellent debugging, analytical, and problem-solving skills.

Good verbal and written communication skills.

Preferred Skills:

Experience with Selenium, Playwright, Cypress, or similar automation tools.

Knowledge of Java, Python, or JavaScript for automation scripting.

Exposure to cloud platforms such as AWS, Azure, or GCP.

Experience working in enterprise-scale automation projects.

ISTQB or equivalent testing certification is an added advantage.

Roles & Responsibilities:

Develop scalable and maintainable automation solutions.

Improve automation coverage and reduce manual testing effort.

Collaborate with stakeholders to deliver quality software releases.

Identify automation improvements and implement best practices.

Support production validation and release testing activities.

Mandatory Skills:

Watermelon Automation Tool

Test Automation

Automation Framework Development

API Testing

SQL

Git

CI/CD

Agile Methodology

Generative AI Engineer 

Role Overview:

You will be responsible for the hands-on development, coding, and deployment of AI-powered features. Your focus is on writing clean, efficient code to integrate LLMs into our existing tech stack, building robust data pipelines for RAG, and ensuring the reliability of model outputs through rigorous testing and optimization.

Key Responsibilities

• Application Implementation: Code and integrate LLM APIs (OpenAI, Anthropic, etc.) or local models into backend services using Python, FastAPI, etc.,
• MCP Server Development: Design and implement custom MCP servers using the official SDKs (Python/TypeScript) to expose internal databases, APIs, and file systems to AI agents.
• RAG Implementation: Build and maintain the "plumbing" for Retrieval-Augmented Generation—specifically coding the data ingestion scripts, text chunking logic, and metadata filtering.
• Vector DB Management: Perform day-to-day operations on vector databases (Pinecone, Milvus, etc.), including indexing, querying, and optimizing search retrieval.
• Prompt Programming: Develop, version-control, and refine complex prompt templates (using Jinja2 or similar) to ensure consistent structured outputs (JSON/YAML).
• Agent Development: Implement multi-step workflows using LangChain, LangGraph, CrewAI etc.,, focusing on tool-calling logic and error handling.
• Evaluation & Testing: Build automated test suites to detect "hallucinations" and measure accuracy using frameworks.
• Performance Tuning: Implement caching layers and streaming responses to reduce latency and improve the end-user experience; Token optimization.
• Data Pre-processing: Clean and tokenize datasets for model fine-tuning or high-quality context retrieval.

Technical Skills (The "Execution" Stack)

• Language: Advanced Python (Asyncio, Pydantic) and optional TypeScript/Node.js (for full-stack integration).
• AI Frameworks: Hands-on experience with any of LangChain, LlamaIndex, and Hugging Face Transformers. RAG and Vector search concepts.
• Data Handling: Proficiency in SQL and handling unstructured data formats (PDFs, Markdown, JSON).
• Deployment: Practical experience with Docker, GitHub Actions (CI/CD), and experience with OpenTelemetry, LangSmith, Weights & Biases etc., Understanding of evaluation/guardrails.
• MCP/API Proficiency: Deep understanding of RESTful APIs, Streaming HTTP, MCP server vs client, JSONRPC

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 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 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.

ABOUT THE ROLE

We are seeking a skilled Mid-Level Python Developer & Test Automation Engineer with 3 to 5 years of experience. In this dual role, you will design and develop robust backend systems while building scalable, automated testing frameworks for both UI and backend application. You will bridge the gap between development and quality assurance, ensuring our software is both highly functional and rigorously tested.

WHAT YOU’LL DO:

• Backend Development: Design, write, and maintain clean, scalable Python code for backend services and microservices.
• API Engineering: Build, integrate, and document secure RESTful or GraphQL APIs.
• Automation Frameworks: Design and maintain UI/Backend automated testing suites for unit, integration, and end-to-end testing.
• CI/CD Integration: Integrate automated test scripts into DevOps pipelines to enable continuous deployment.
• Bug Detection: Identify, log, and track software defects while collaborating with teams to resolve them quickly.
• Code Quality: Participate in code reviews to enforce PEP 8 standards, maintainability, and security best practices.

YOUR QUALIFICATIONS:

• Python Expertise: 3+ years of professional software development experience using core Python and Object-Oriented Programming (OOP).
• Web Frameworks: Hands-on experience with at least one major Python web framework (FastAPI, Django, or Flask).
• Testing Frameworks: Strong proficiency with automated testing tools like pytest, unittest, Selenium. Knowledge in Playwright is nice to have.
• AI assisted testing tools/frameworks: Nice to have experience with AI assisted testing tools or framework.
• Database Management: Solid experience writing complex queries for relational (PostgreSQL, MySQL) or NoSQL (MongoDB) databases.
• DevOps & Tools: Proficient with Git version control and CI/CD platforms like Jenkins, GitHub Actions, or GitLab CI.
• Agile Methodology: Experience working in an Agile/Scrum environment with tools like Jira or Confluence. 

Preferred Qualifications

• Experience with containerization technologies like Docker and Kubernetes.
• Familiarity with cloud platforms such as AWS, Azure, or GCP.
• Knowledge of Performance and Load Testing tools like Locust or JMeter

BENEFITS

• Competitive salaries
• 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.

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.

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 Title : MLOps Engineer

Mode: Hybrid

Experience : 4 to 7 Years

Location : Hyderabad (Priority)/Bengaluru locations only

Notice Period : Immediate Joiner

Job Summary:

We are looking for a skilled and proactive ML Engineer with strong expertise in Python, Databricks, and Machine Learning model development. The ideal candidate should be proficient in building scalable data pipelines and deploying ML models, with a working knowledge of MLOps principles and tooling. This role offers an opportunity to work on impactful AI/ML initiatives in a collaborative environment.

Key Responsibilities:

• Develop and maintain machine learning pipelines for training, testing, and deploying models

• Design and implement infrastructure for managing and monitoring machine learning models

• Work with data scientists to build scalable, efficient, and automated model training and testing processes

• Collaborate with software engineers to integrate machine learning models into production systems

• Automate and optimize the deployment and scaling of machine learning models in a distributed computing environment

• Monitor and troubleshoot machine learning systems and infrastructure to ensure high availability and performance

• Develop and maintain documentation and best practices for MLOps processes and procedures.

Experience:

Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field

• 3+ years of experience in MLOps or related field, including building and deploying machine learning models at scale

•Proficiency in programming languages such as Python, Java, and C++

•Experience with machine learning frameworks such as TensorFlow, PyTorch, and Keras

• Experience with containerization technologies such as Docker and Kubernetes

• Strong understanding of DevOps principles and practices

• Experience with cloud computing platforms such as AWS, Azure, or Google Cloud

About the Programme 

Developing an enterprise AI platform focused on financial compliance and intelligence.  

Role Overview 

We are looking for a strong Data Engineer to own the data foundation of the platform. Every model, every AI output, and every compliance decision the system makes depends on data arriving reliably, completely, and on time. You will design and build the ingestion pipelines from all source systems into the data platform, own the pipeline monitoring infrastructure, and work closely with internal IT and operations teams to extract data from complex enterprise source 

systems.  

Key Responsibilities 

Data Discovery & Audit 

• Conduct a thorough data audit with internal IT and operations teams — map every data 

source needed for the platform, assess what already exists on the data platform, and identify gaps. 

• Document data sources, schemas, update frequencies, and quality issues for all relevant datasets  

• Raise data gaps and quality risks to the Solutions Architect  

Pipeline Design & Build 

• Design and build ingestion pipelines from all source systems — ERP, government portals, supplier portals, and banking feeds — into the data platform. 

• Design pipelines for both batch and real-time ingestion patterns 

• Ensure pipelines are idempotent, resumable, and handle source system failures gracefully without data loss or duplication. 

Data Quality & Reliability 

• Build pipeline monitoring and alerting so data failures are caught and flagged before they corrupt model training or inference. 

• Define and implement data quality checks at the point of ingestion — schema validation, completeness checks, and anomaly detection on incoming data volumes. 

• Maintain clear data lineage so the team always knows where a data point came from and when it was last updated. 

Collaboration & Handoff 

• Work closely with internal IT and automation team who hold institutional knowledge of the source systems — this is not a solo exercise. 

• Hand off clean, well-documented datasets to the ML Engineers and LLM Engineer for model training and knowledge base building. 

• Support the MLOps Engineer in ensuring production pipelines are stable and monitored post-deployment. 

Required Qualifications 

Education 

•B.E. / B.Tech / M.Tech in Computer Science, Information Technology, or a related field. 

Experience 

• 5+ years of data engineering experience with at least 2 years working on production pipelines at enterprise scale. 

• Demonstrated experience building pipelines from complex enterprise source systems — ERP or equivalent. 

• Experience building both batch and real-time / streaming ingestion pipelines. 

Technical Skills 

• Languages: Python and PySpark; SQL proficiency essential. 

• Data Platform: Databricks and Delta Lake — must have hands-on production experience. 

• Pipeline Orchestration: Apache Airflow, Databricks Workflows, or equivalent. 

• Streaming: Kafka, Spark Structured Streaming, or equivalent for real-time ingestion patterns. 

• ERP Integration: Experience extracting data from SAP or equivalent large ERP systems strongly preferred. 

• API Integration: REST API consumption for government portal or third-party data feeds. 

• Data Quality: Experience with data quality frameworks — Great Expectations or equivalent. 

• Observability: Pipeline monitoring, alerting, and data lineage tooling. 

Preferred Qualifications 

• Familiarity with SAP data models  

• Prior experience with government API ecosystems — GSTN, ICEGATE, or similar. 

• Experience building pipelines that feed ML model training workflows. 

• Exposure to Unity Catalog or similar data catalogue and governance tools. 

• Prior work in fintech, compliance, or tax technology environments. 

We are seeking Senior Backend Engineers who can hack around multiple technologies and build highly scalable, low-latency, distributed systems with RESTful services. You will be responsible for developing new software products (internal) and solving complex technical challenges for scale-ups and enterprise companies. You should excel in working with large-scale applications and frameworks and have outstanding communication and leadership skills.

You’ll be responsible for—

• Writing clean, high-quality, high-performance, and maintainable code.
• Solving complex technical problems.
• Performing an objective analysis of the problem statement and coming up with an unbiased technical solution before writing a single line of code.
• Coordinating cross-functionally to ensure the project meets business objectives and compliance standards.
• Participating in and driving code reviews.
• Building robust, secure, and scalable microservices.
• Implementing RESTful services with a metric-driven API Gateway.
• Ensuring sub-second server response and will be responsible for implementing relational, document, key, object, or graph data stores, index stores and messaging stores as needed.
• Tracking defects and work with business owners and users to triage bugs and manage backlog
• Taking ownership to run and maintain Cloud infrastructure.
• Evaluating relevant technologies, influencing and driving architecture and design discussions.
• Architecting & designing the platform.
• Mentoring junior engineers helping them grow, performing code reviews, system monitoring & delegation.
• Writing documentation & create engineering processes.
• Working as an individual contributor.
• Helping drive KPIs with Product.
• Taking ownership of backend systems.
• Helping with unit tests & QA process.
• Scaling the engineering team.
• System scaling to hundreds of millions of users.
• Working with product managers and designs.

Requirements

What you need—

• Overall 5-8 years of experience in software development with a strong base in Golang/Java/Python and a degree in Computer Science(optional).
• Experience with Go, K8, Docker, AWS, and CI/CD.
• Experience with micro web frameworks – like Springboot, Gin/Mux.
• Experience in working with microservice architectures, Transactional systems, and Distributed environments.
• Exposure to building RESTful APIs with monitoring, fault tolerance, and metrics (with something like Hystrix).
• Experience with MySQL, and NoSQL (Cassandra, Redis, DynamoDB).
• Experience in server-side services using ElasticSearch and ESB - Camel, ActiveMQ.
• DevOps experience.
• Experience with AWS stack.
• Excellent attention to detail.
• Outstanding written and verbal communication skills.
• To be a self-starter who can work well with minimal to no guidance in a fluid environment.
• To be excited by challenges surrounding the development of highly scalable & distributed systems.
• To be agile and able to adapt quickly to changing requirements scope and priorities.
• To be experienced in working on massively large-scale data systems in production environments.
• To have led or mentored an engineering team before(optional).
• To have contributed to open-source projects.
• To have a strong drive & desire for continued growth.
• Proficiency in English.
• Experience with Web3(optional).

Benefits

What you get—

• Best-in-class salary: We hire strong talent and compensate accordingly.
• Meet and learn from designers, engineers, product leaders, and AI practitioners.
• Continuous learning: Work with a world-class team and stay close to the latest in AI, engineering, and product development.
• High-impact work: Build AI-first systems and products used at scale by global clients.

Job Title: Senior Data Tester

Location : Hyderabad

Mode: Hybrid

Notice Period: Immediate Joiner

Key Responsibilities:

• 8+ years of experience in ETL/data testing.
• Design, implement, and execute data validation test plans and test cases.
• Understanding of data modelling and data governance principles.
• Experience with test automation frameworks and scripting (e.g., Python, Shell)Conduct thorough ETL testing, including data extraction, transformation, and loading.
• Validate data integrity across various sources and destinations (data lakes, warehouses, etc.)
• Perform data reconciliation and analysis to identify inconsistencies or data quality issues.
• Develop and maintain automated data testing frameworks using SQL or scripting languages.
• Strong experience with SQL and writing complex queries for data validation.
• Knowledge of data warehouse concepts and testing tools. Experience with ETL tools (e.g., Informatica, Talend, SSIS, etc.)
• Familiarity with cloud platforms (Azure, GCP) and modern data tools (e.g., Snowflake, Big Query).
• GCP is mandatory. Experience in Agile development and working within cross-functional teams.
• Exposure to BI tools (Power BI, Tableau, Looker)
• Familiarity with CI/CD pipelines and version control systems like Git ISTQB or equivalent testing certifications.

• Design, build, and maintain scalable ETL/ELT pipelines for batch and real-time data ingestion and transformation.
• Develop and optimize data lake and data warehouse architectures (e.g., Snowflake, BigQuery, Redshift).
• Work with cloud platforms GCP, Azure to manage data infrastructure.
• GCP as mandatory skills
• Collaborate with analytics and product teams to understand data needs and deliver solutions.
• Ensure data quality, reliability, security, and compliance across all data systems.
• Mentor junior data engineers and contribute to best practices and code reviews.
• Monitor and troubleshoot data pipeline performance and resolve data-related issues.
• Automate data validation, monitoring, and alerting processes.
• 8+ years of experience in data engineering or software engineering with a data focus.
• Proficient in SQL and at least one programming language (e.g., Python, Scala, Java).
• Experience with modern data warehousing tools (e.g., Snowflake, Redshift, BigQuery).
• Strong understanding of data modeling, data lakes, and ETL/ELT design.
• Hands-on experience with orchestration tools like Airflow, dbt, or similar.
• Solid experience with cloud data platforms (AWS/GCP/Azure).
• Familiarity with CI/CD pipelines, containerization (Docker/Kubernetes), and version control (Git).
• Experience working in a DevOps or DataOps environment.
• Knowledge of data governance, lineage, and cataloging tools (e.g., Collibra, Alation).
• Familiarity with streaming technologies (Kafka, Spark Streaming, Flink).
• Experience supporting machine learning workflows and data science initiatives.

Full Stack Developer

Experience: 3 to 5 Years

Location: Hyderabad

Interview Mode: Face-to-Face (F2F)

Work Mode: Hybrid

Employment Type: Full-Time

Note: Immediate joiners or candidates with a notice period of up to 15 days are preferred. We are only considering candidates who can attend a face-to-face interview at our office.

Role Summary;

We are looking for a Full Stack Developer (React.js + Python + AWS + AI) to design, develop, and maintain scalable web applications and AI-powered solutions. The ideal candidate must have strong experience in React.js, Python, AWS, and AI development. Hands-on AI development experience is mandatory for this role.

Mandatory AI Skill Requirement - AI development experience is mandatory. Candidates without hands-on AI development experience will not be considered.

Candidates must have practical experience in one or more of the following:

• Building AI-powered applications.
• Working with Large Language Models (LLMs).
• Prompt Engineering.
• Retrieval-Augmented Generation (RAG).
• AI agent development using LangChain, LangGraph, or similar frameworks.
• Integrating AI models through APIs such as OpenAI, Anthropic, or Gemini.
• Developing AI-enabled workflows and intelligent business applications.

Note: Experience using AI coding assistants (ChatGPT, GitHub Copilot, Claude, Gemini, etc.) alone does not meet this requirement. Candidates must have actual AI application development experience.

Key Responsibilities

• Develop and maintain full-stack web applications.
• Build responsive frontend applications using React.js.
• Develop backend REST APIs using Python (FastAPI / Django / Flask).
• Design, develop, and integrate AI-powered features into applications.
• Develop and deploy applications on AWS cloud services.
• Work with relational databases (MySQL / PostgreSQL / SQL Server).
• Integrate frontend and backend components.
• Follow Agile/Scrum development processes.
• Write clean, maintainable, and production-ready code.
• Collaborate with cross-functional teams to deliver scalable and high-quality solutions.

Required Skills (Mandatory)

• 3 to 5 years of Full Stack development experience.
• Strong experience in React.js.
• Strong backend development experience in Python.
• Hands-on experience with AWS cloud services.
• Hands-on AI development experience (Mandatory).
• Experience with LLMs, Prompt Engineering, RAG, LangChain, LangGraph, or similar AI frameworks.
• REST API development experience.
• Strong SQL database experience.
• Git and Agile development workflow.
• Good understanding of software design principles and best practices.

Good to Have

• Docker
• CI/CD pipelines
• Microservices architecture
• Kubernetes
• Experience with vector databases and AI model integrations

Preferred Qualifications

• Bachelor's degree in Computer Science or a related field.
• Strong problem-solving and analytical skills.
• Good communication and teamwork abilities.
• Passion for learning new technologies, especially AI, cloud technologies, and modern software development.

Job Description:

We are seeking a highly skilled Machine Learning Engineer to join our team. The ideal candidate will have a strong background in Natural Language Processing (NLP), Large Language Models (LLMs), and Python programming.

You will work closely with data scientists, product managers, and data engineers to design, develop, and deploy high-performance AI/ML models and integrate generative AI solutions into existing workflows.

Your responsibilities will include:

• Collaborating with cross-functional teams to design and deliver high-performance AI models, including NLP, computer vision, semantics engines, linguistic analysis, risk management, and time-series prediction models. Integrating generative AI solutions into existing workflow systems.
• Developing and maintaining the ML Operations CI/CD pipeline for seamless deployment and monitoring. Training, tuning, and optimizing AI models and algorithms for enhanced performance.
• Implementing complex real-time data and AI/ML applications to capture knowledge and automate decision-making processes.
• Creating ML/AI models for business teams and establishing metrics to track their accuracy and performance. Overseeing the full lifecycle of algorithm development, from ideation to deployment and monitoring. Evaluating and ranking ML algorithms based on their potential success in solving specific problems.
• Serving as an internal resource for AI/ML needs, providing guidance and insights to stakeholders during strategic discussions.

Required Experience and Skills:

Machine Learning:

• Proficient in generative AI techniques, prompt engineering, and Retrieval-Augmented Generation (RAG) (3+ years).
• Experience with Large Language Models (LLMs) such as OpenAI, Gemini, LLAMA, and other state-of-the-art models (3+ years).
• Expertise in using ML/AI libraries such as Pandas, NumPy, PyTorch, TensorFlow, Keras, BERT, LayoutLM, and traditional ML algorithms (5+ years).
• Experience with distributed ML/AI training libraries/models: Koalas, Horovod, DDP.

Python Programming and Software Engineering:

• Expertise in Pythonic clean coding practices, including the use of decorators, generators, and descriptors (5+ years).
• Strong understanding of software design principles such as DRY, OAOO, YAGNI, KIS, EAFP/LBYL, and defensive programming (2+ years).
• Proficient in software design concepts focusing on cohesion and coupling (2+ years). Knowledge of SOLID principles (2+ years).

Education and Experience:

• Minimum Bachelor's degree or foreign equivalent in Computer Science, Electrical Engineering, or a closely related field.
• At least 5 years of experience as a software engineer and 5 years of ML-related programming.

Job Description:

Position: Senior Data Engineer

Location: Chennai / Pune / Bangalore / Hyderabad

Working Type: WFO

Shift: UK Shift (2:00 – 11:00 PM)

Experience : 7+ years overall

Interviews: Assessment || 2 Interview rounds.

Notice Period: Immediate Joiner

Key Responsibilities

Implement ingestion, transformation, and optimization of enterprise data sources into Microsoft Fabric Lakehouse environments.

Configure and optimize Fivetran connectors (Oracle, SQL DB, etc.)

Manage large-volume ingestion and backfill operations

Implement Bronze to Silver transformation pipelines

Develop incremental load and CDC logic

Optimize Lakehouse performance and storage patterns

Implement monitoring (record counts, load duration, failure tracking)

Support Dev/Test/Prod promotion processes

Required Qualifications

7+ years of data engineering experience

Hands-on experience with Microsoft Fabric or Azure Synapse/Data Factory

Strong experience with Fivetran or similar ELT tools

Experience handling high-volume datasets (hundreds of millions of records)

Proficiency in SQL, Python, and data modeling concepts

Strong understanding of Medallion architecture.

Location: Ahmedabad / WFO

Experience: 0–1 Year / Freshers

Internship: 6months ( leading to full-time employment )

About the Role

We are looking for a motivated DevOps Engineer Intern who is interested in Cloud, Automation, and Infrastructure. You will work with our engineering team on application deployments, cloud environments, CI/CD pipelines, and day-to-day DevOps activities.

What You'll Work On

• Work with Linux-based systems and perform basic troubleshooting.
• Use Git & GitHub for version control and team collaboration.
• Write basic Bash/Shell scripts to automate repetitive tasks.
• Work with Docker to build and run containerized applications.
• Assist with CI/CD pipelines and automated application deployments.
• Gain hands-on exposure to AWS, Azure, or GCP.
• Understand basic cloud networking, IAM, and security concepts.
• Troubleshoot deployment, application, and infrastructure issues.
• Maintain basic technical documentation and follow DevOps best practices.

Must-Have Skills

• Basic understanding of Linux.
• Hands-on knowledge of Git & GitHub.
• Basic Bash/Shell scripting.
• Understanding of Docker and containers.
• Basic understanding of at least one cloud platform (AWS/Azure/GCP).
• Understanding of CI/CD concepts.
• Basic networking concepts, including HTTP/HTTPS, DNS, ports, IP, and SSH.
• Good troubleshooting and problem-solving skills.
• Strong interest in DevOps, Cloud, and Automation.

Good to Have

• Hands-on experience with GitHub Actions.
• Basic knowledge of Terraform.
• Exposure to Kubernetes.
• Basic Python scripting.
• Personal DevOps/Cloud projects or a GitHub portfolio.
• AWS/Azure/GCP or DevOps certification.

About the Programme

Developing an enterprise AI platform focused on financial compliance and intelligence

Role Overview

We are looking for a QA Engineer to own testing across the full platform. In a compliance context, a software defect is not just a technical issue — it carries real financial and regulatory consequences. You will be involved from the earliest stages of development, defining acceptance criteria, building test frameworks, and ensuring that every component of the system meets the accuracy and reliability standards required before it goes near a production compliance workflow.

Key Responsibilities

Test Strategy & Planning

•       Develop and own the overall test strategy for the platform, covering all layers — data pipelines, ML models, APIs, application services, and frontend interfaces.

•       Work with the Product Manager to define acceptance criteria for every user story before development begins, not after.

•       Define accuracy and reliability thresholds for each ML model in scope — what performance levels are required before a model is considered production-ready.

•       Maintain a living test plan that evolves with the product as new use cases are added.

Functional & Business Logic Testing

•       Translate compliance rules and business logic into structured, executable test cases.

•       Work closely with the subject matter experts to identify edge cases and exception scenarios that automated tests must cover.

•       Validate that AI outputs — channel routing decisions, classification recommendations, and generated responses — are correct against defined compliance ground truth.

•       Test end-to-end workflows from data ingestion through to user-facing outputs, ensuring correctness at every stage.

ML & AI System Testing

•       Build regression test suites for all deployed models so that retraining does not break existing correct behaviours.

•       Test model outputs for consistency, groundedness, and accuracy using both automated evaluation and structured manual review.

•       Validate fallback mechanisms — ensure the system behaves correctly when model confidence is low or an upstream service is unavailable.

•       Test feedback loop integrity — verify that validator decisions are correctly captured and routed to the model retraining pipeline.

API & Integration Testing

•       Build and maintain API test suites for all integration endpoints — covering correctness, error handling, and edge cases.

•       Perform load and performance testing on real-time inference endpoints to validate behaviour under expected production volumes.

•       Test all third-party and government portal integrations against documented API contracts, including failure and retry scenarios.

User Acceptance & Production Readiness

•       Coordinate and facilitate user acceptance testing with the RIL compliance and assurance teams.

•       Produce a formal production readiness assessment before each deployment, documenting what has been tested, what passed, and any known residual risks.

•       Monitor for defects in production and manage the defect lifecycle through to resolution.

Required Qualifications

Education

•       B.E. / B.Tech / M.Tech in Computer Science, Information Technology, or a related field.

Experience

•       4+ years of QA engineering experience with at least 2 years testing AI, ML, or data-heavy systems in production.

•       Experience writing and maintaining automated test frameworks, not just manual testing.

•       Demonstrated ability to translate complex business rules into structured test cases.

Technical Skills

•       Test Automation: Pytest, Selenium, Playwright, or equivalent for backend and frontend automation.

•       API Testing: Postman, REST-assured, or equivalent; experience with contract testing.

•       Performance Testing: Locust, JMeter, or equivalent for load and stress testing.

•       ML Testing: Experience validating ML model outputs, building evaluation datasets, and running regression suites against retrained models.

•       Data Testing: Experience validating data pipeline outputs — schema checks, completeness, and consistency.

•       Languages: Python for test scripting and automation.

•       CI Integration: Experience integrating test suites into CI/CD pipelines for automated test execution.

Preferred Qualifications

•       Prior experience testing compliance, fintech, or legal technology systems where business logic accuracy is critical.

•       Familiarity with LLM evaluation frameworks — RAGAS, TruLens, or equivalent.

•       Experience with exploratory testing techniques for AI systems where outputs are probabilistic.

•       Prior exposure to regulated environments where formal sign-off and test documentation are required.

* Strong practical knowledge and interest in modern AI tools.

* Genuine curiosity and willingness to continuously learn.

* Ability to research, experiment, implement, troubleshoot and improve independently.

* Good understanding of prompting and AI workflows.

* Strong problem-solving mindset.

* Basic understanding of APIs, integrations and automation.

* Ability to explain technology clearly to non-technical people.

* Comfortable using AI to solve real-world business problems.

Hiring for Data Analyst

Exp : 5 - 7 yrs

Edu : BE/B.Tech

Work Location : Noida WFO

Skills :

Expertise in SQL Server, including database design, performance tuning, query optimization, and security.

Hands-on experience developing ETL solutions using SSIS, Azure Data Factory (ADF), and Python.

About the Programme

Developing an enterprise AI platform focused on financial compliance and intelligence.

Role Overview

We are looking for a Backend Engineer to own the integration layer of the platform. You will build the APIs and services that connect AI outputs back into operational workflows, integrate with government portals and external data sources, and ensure that the system interacts reliably with RIL’s existing infrastructure. This role requires equal measures of technical rigour and pragmatism to deliver reliably within a fast-moving build timeline.

Key Responsibilities

Integration Mapping & Design

•       Work with the Solutions Architect to map every integration touchpoint — ERP workflow interfaces, government portals, supplier portals, and banking feeds.

•       Understand the latency requirements for each integration and design accordingly — real-time endpoints and batch integrations have fundamentally different design constraints.

•       Design robust fallback mechanisms for every integration — if the AI model is unavailable or low-confidence, the system must degrade gracefully without blocking operational workflows.

Real-Time API Development

•       Design and implement the API layer that connects the AI inference layer to operational systems — clean contracts, versioned endpoints, and clear error responses.

•       Ensure all real-time integrations are built with resilience patterns — circuit breakers, retry logic, timeouts, and graceful degradation under load.

Government Portal & External Integrations

•       Build integrations with government portal APIs for inward supply data, vendor return filing status, and notice data where available via official channels.

•       Build the integration with the customs data portal

•       Manage the specific constraints of government API ecosystems — rate limits, authentication flows, schema changes, and reliability issues that differ significantly from commercial APIs.

Monitoring & Production Stability

•       Build API monitoring and alerting covering latency, error rates, and upstream availability so integration failures are caught before they affect the compliance team.

•       Work with the MLOps Engineer to ensure model inference endpoints are stable and performant enough for the real-time integration to depend on.

•       Maintain clear API documentation so the Full Stack Engineer and other squad members can consume integrations without dependency.

Required Qualifications

Education

•       B.E. / B.Tech / M.Tech in Computer Science, Information Technology, or a related field.

Experience

•       5+ years of backend engineering experience with a focus on API design and enterprise system integration.

•       Proven experience building and maintaining production-grade integrations with complex or legacy enterprise systems.

•       Experience with real-time, latency-sensitive API development in an operational context.

Technical Skills

•       Languages: Python (primary); working knowledge of at least one of Java, Go, or Node.js.

•       API Design: REST API design principles, versioning, authentication (OAuth 2.0, API keys, mTLS), and contract-first development.

•       Resilience Patterns: Circuit breakers, retry logic, rate limiting, bulkheads, and graceful degradation.

•       ERP Integration: Experience integrating with SAP or equivalent large ERP systems — BAPIs, RFC calls, or event-driven extraction patterns strongly preferred.

•       Async & Messaging: Kafka, RabbitMQ, or equivalent for event-driven integration patterns.

•       Containerisation: Docker and Kubernetes for deploying and managing API services.

•       Observability: API monitoring, distributed tracing (Jaeger, Zipkin, or equivalent), and structured logging.

Preferred Qualifications

•       Prior experience with government API ecosystems — GSTN, ICEGATE, or similar; understanding of their specific constraints and reliability characteristics.

•       Experience building integrations that serve ML model inference endpoints in a production setting.

•       Familiarity with SAP integration patterns — SAP BTP, OData services, or SAP API Hub.

•       Prior work in fintech, compliance, legal technology, or similarly regulated environments.

•       Experience with API gateway tooling — Kong, AWS API Gateway, or equivalent.

Strong Azure Databricks Engineer / Senior Data Engineer Profile

2

Mandatory (Experience 1) – Must have minimum 8+ years of overall experience in Data Engineering, Data Development, or related data technology roles, with strong hands-on experience in enterprise data pipeline development.

3

Mandatory (Experience 2) – Must have strong hands-on experience with Azure Databricks, including development and optimization of scalable data engineering workflows using Databricks and PySpark.

4

Mandatory (Experience 3) – Must have strong hands-on proficiency in PySpark/Python and SQL, with proven experience developing complex data transformations, processing workflows, and performance-optimized queries.

5

Mandatory (Experience 4) – Must have hands-on experience with Azure Data Factory (ADF) for designing, developing, and orchestrating end-to-end data pipelines and integrating data from multiple sources.

6

Mandatory (Experience 5) – Must have strong experience working on the Azure Cloud platform and associated data services, with solid understanding of data warehousing, data modeling, pipeline architecture, and enterprise data solutions.

7

Mandatory (Experience 6) – Must have hands-on experience implementing CI/CD using Azure DevOps, including deployment and release management across development, QA, and production environments.

8

Mandatory (Experience 7) – Must have proven technical leadership experience, including code reviews, enforcing development best practices, mentoring developers, and providing technical guidance to a data engineering team.

9

Mandatory (Notice Period) – Immediate joiners or candidates who can join within 15 days.

10

Mandatory (Note) - The position is open across all Cognizant offices pan India. Candidates must be willing to attend the F2F interview at the nearest Cognizant office location.

Roles & Responsibilities

• Design, develop, and deliver scalable end-to-end data pipelines using Azure Data Factory, ensuring robust integration

of enterprise-wide data from diverse sources

• Build and optimize data engineering workflows using Databricks and PySpark

• Write efficient, high-performance SQL for data transformation and analysis

• Work with the Azure Cloud platform and associated services, applying strong understanding of data warehousing,

data models, and pipelines

• Provide technical leadership to a team of developers, including code reviews and enforcing best practices across the

development lifecycle

• Oversee CI/CD implementation using Azure DevOps, managing deployments across development, QA, and production

environments with proper change control processes

• Collaborate with cross-functional teams to translate business requirements into scalable data solutions

• Ensure data quality, reliability, and performance across all pipelines and platforms

Ideal Candidate

1Strong Azure Databricks Engineer / Senior Data Engineer Profile

2Mandatory (Experience 1) – Must have minimum 8+ years of overall experience in Data Engineering, Data Development, or related data technology roles, with strong hands-on experience in enterprise data pipeline development.

3Mandatory (Experience 2) – Must have strong hands-on experience with Azure Databricks, including development and optimization of scalable data engineering workflows using Databricks and PySpark.

4Mandatory (Experience 3) – Must have strong hands-on proficiency in PySpark/Python and SQL, with proven experience developing complex data transformations, processing workflows, and performance-optimized queries.

5Mandatory (Experience 4) – Must have hands-on experience with Azure Data Factory (ADF) for designing, developing, and orchestrating end-to-end data pipelines and integrating data from multiple sources.

6Mandatory (Experience 5) – Must have strong experience working on the Azure Cloud platform and associated data services, with solid understanding of data warehousing, data modeling, pipeline architecture, and enterprise data solutions.

7Mandatory (Experience 6) – Must have hands-on experience implementing CI/CD using Azure DevOps, including deployment and release management across development, QA, and production environments.

8Mandatory (Experience 7) – Must have proven technical leadership experience, including code reviews, enforcing development best practices, mentoring developers, and providing technical guidance to a data engineering team.

9Mandatory (Notice Period) – Immediate joiners or candidates who can join within 15 days.

10Mandatory (Note) - The position is open across all Cognizant offices pan India. Candidates must be willing to attend the F2F interview at the nearest Cognizant office location.

About the company

The client is building an AI-native platform for modern law firms.

Legal work is evolving rapidly, but much of the industry's workflow continues to rely on fragmented software and manual processes. The team is building an intelligent platform that helps law firms streamline work across the entire legal lifecycle, including document screening, drafting, filing, research, compliance, and other high-value legal workflows.

Their founding team combines deep expertise across both law and technology, giving us a unique perspective on how AI can fundamentally reshape legal operations while maintaining the precision and reliability that the profession demands.

We're looking for engineers who want to build products from first principles, move quickly, and help define the future of AI in legal technology.

About the role

We're hiring a Lead Engineer to help build the core platform from the ground up.

This is a high-ownership role. You'll work across backend systems, AI infrastructure, product architecture, and deployment, collaborating directly with the founders to design and ship features that reach customers quickly.

If you enjoy solving hard engineering problems, shipping fast, and working in a small, ambitious team, we'd like to talk.

What You'll Build

• AI-powered legal workflows for law firms
• Agentic systems for drafting, screening, and legal document analysis
• Reliable backend services and APIs
• Retrieval and knowledge systems for legal intelligence
• Evaluation pipelines to improve AI quality and reliability
• Internal developer tooling and scalable platform infrastructure

What We're Looking For

• Around 5 years or more of software engineering experience
• Strong Proficiency in at least one backend language: Python (FastAPI or Django), or Node.js
• Experience working with at least one database: MongoDB, PostgreSQL, MySQL, or another relational database
• Understanding of modern AI application architecture, including concepts such as: Agentic systems AI SDKs Retrieval-Augmented Generation (RAG) Prompt engineering Evaluation frameworks (Evals)
• Strong system design and problem-solving skills
• Ability to ship production-quality software quickly
• High ownership and bias toward execution
• Comfortable learning new technologies as the product evolves

Nice to Have

• Experience building products in Compliance, FinTech, RegTech, or LegalTech
• Experience deploying and operating AI-powered production systems
• Familiarity with cloud platforms and modern deployment workflows
• Experience working in an early-stage startup
• Experience leading teams or mentoring people

What We Value

• High agency
• Strong engineering fundamentals
• Curiosity and continuous learning
• Fast execution without compromising quality
• Clear communication and collaborative problem solving

Why Join Us

• You'll be joining at the earliest stage of the company and will help shape both the product and the engineering culture.
• This is an opportunity to work directly with founders who combine legal and technical expertise, solve meaningful problems for the legal industry, and build AI systems that are used every day by legal professionals.
• If building from zero excites you more than maintaining legacy systems, we'd love to hear from you

Hiring : Senior Databricks AI Architect

Exp : 15 - 18 yrs

Work Location : Pune WFO

Skills :

10 +years of experience in Data Engineering, Data Architecture, Analytics, or Software Engineering.

Minimum 5 years of hands-on experience with Databricks (Mandatory).

Strong expertise in designing and implementing enterprise-scale data platforms on Databricks.

Hands-on experience with AI-powered engineering tools such as Databricks Genie, Cursor, GitHub Copilot, or similar AI platforms.

Strong proficiency in Python, SQL, Spark, Delta Lake, and Databricks notebooks.

Excellent communication, stakeholder management