Head AI Systems & Operations

Job Title: Full Stack AI Engineer

Location: Remote/Hyderabad

Experience Level: 3-5

Salary Range: 12-18LPA

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

Description:

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

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

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

Requirements:

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

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

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

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

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

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

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

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

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

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

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

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

Build AI where the work actually happens.

Celeco works inside real businesses to understand critical workflows, ship production AI systems and stay through adoption.

We are hiring our first Intelligence Architect (Forward-Deployed AI Engineer).

⌁

Full-time · Remote-first · Optional hybrid in Bengaluru

At least one year of professional engineering experience. Customer travel when the work requires it.

The role

An Intelligence Architect enters a customer environment with an unfinished question and leaves behind a working, measurable system.

This is a hands-on engineering role at the boundary of product, operations and customer delivery. You will learn the domain, inspect the existing systems and data, decide what should be built, and write the code that puts it into production.

What you will own

• Interview and shadow the people doing the work, then map the real workflow, including hand-offs, exceptions and workarounds.
• Understand the customer's application stack, APIs, data, identity model, security constraints and deployment environment.
• Turn business goals into a technical scope, system design, delivery plan and measures of success.
• Build across the stack: AI workflows, data pipelines, integrations, backend services and the interfaces people use.
• Choose the simplest reliable approach. The answer may combine agents, retrieval, rules and conventional software.
• Create evals from representative cases, define quality and failure metrics, inspect traces, red-team the system and set launch thresholds.
• Make practical trade-offs across accuracy, latency, cost, privacy, security and speed.
• Take prototypes into production with tests, monitoring, access controls, documentation and a plan for failure and recovery.
• Work beside customer teams during rollout and improve the system until it becomes part of the workflow.
• Turn what works into reusable components, evaluation sets and playbooks for future Celeco deployments.

You will probably thrive here if

• You have at least one year of professional software or product engineering experience.
• You have shipped a real system used by other people and can explain what you owned, what broke and what you changed.
• You are strong in Python or TypeScript and comfortable moving across unfamiliar codebases, APIs, databases and cloud services.
• You have built with language or multimodal models and understand prompting, structured outputs, retrieval, tool use and model failure modes.
• You use Cursor, Codex, Claude Code or similar tools as part of your engineering workflow, while still reviewing, testing and understanding the code you ship.
• You can create an evaluation set, choose useful quality metrics and improve a system through error analysis instead of prompt guesswork.
• You can speak with an operator, an engineering team and a senior leader without losing the thread of the problem.
• You work well with incomplete requirements, write clearly and surface risks early.
• You care about whether people use what you build and whether it changes a business outcome.
• You can commit full-time and travel to customer sites when discovery or rollout is better done in person.

Experience with cloud deployment, containers, CI/CD, observability, enterprise integrations, authentication or security is useful. We do not expect one person to arrive knowing every framework, cloud or industry.

What you will get

• Direct ownership of live customer problems from discovery through production.
• Close collaboration with Celeco's founders and customer leadership teams.
• Exposure to different industries, operating models and technical environments.
• The freedom to choose the technical approach and the responsibility to prove that it works.
• A role in defining Celeco's engineering methods, reusable systems and technical culture while the company is early.
• A remote-first setup, with the option to work together in Bengaluru.

If the work sounds like you but your background is unconventional, apply. We care more about what you have built, how you think and how quickly you learn than pedigree.

About the Role

We are seeking a hands-on Tech Lead to design, build, and integrate AI-driven systems that automate and enhance real-world business workflows. This is a high-impact role for someone who enjoys full-stack ownership — from backend AI architecture to frontend user experiences — and can align engineering decisions with measurable product outcomes.

You will begin as a strong individual contributor, independently architecting and deploying AI-powered solutions. As the product portfolio scales, you will lead a distributed team across India and Australia, acting as a System Integrator to align engineering, data, and AI contributions into cohesive production systems.

Example Project

Design and deploy a multi-agent AI system to automate critical stages of a company’s sales cycle, including:

• Generating client proposals using historical SharePoint data and CRM insights
• Summarizing meeting transcripts
• Drafting follow-up communications
• Feeding structured insights into dashboards and workflow tools

The solution will combine RAG pipelines, LLM reasoning, and React-based interfaces to deliver measurable productivity gains.

Key Responsibilities

• Architect and implement AI workflows using LLMs, vector databases, and automation frameworks
• Act as a System Integrator, coordinating deliverables across distributed engineering and AI teams
• Develop frontend interfaces using React/JavaScript to enable seamless human-AI collaboration
• Design APIs and microservices integrating AI systems with enterprise platforms (SharePoint, Teams, Databricks, Azure)
• Drive architecture decisions balancing scalability, performance, and security
• Collaborate with product managers, clients, and data teams to translate business use cases into production-ready systems
• Mentor junior engineers and evolve into a broader leadership role as the team grows

Ideal Candidate Profile

Experience Requirements

• 5+ years in full-stack development (Python backend + React/JavaScript frontend)
• Strong experience in API and microservice integration
• 2+ years leading technical teams and coordinating distributed engineering efforts
• 1+ year of hands-on AI project experience (LLMs, Transformers, LangChain, OpenAI/Azure AI frameworks)
• Prior experience in B2B SaaS environments, particularly in AI, automation, or enterprise productivity solutions

Technical Expertise

• Designing and implementing AI workflows including RAG pipelines, vector databases, and prompt orchestration
• Ensuring backend and AI systems are scalable, reliable, observable, and secure
• Familiarity with enterprise integrations (SharePoint, Teams, Databricks, Azure)
• Experience building production-grade AI systems within enterprise SaaS ecosystems

Role Overview 

We are looking for an AI Engineer to design, build, and ship production AI systems, including agentic AI applications, for enterprise clients. This is a hands-on engineering role: you will write production code, build and evaluate models and agents, and work closely with architects and product teams to take solutions from prototype to scale. 

Key Responsibilities 

Design and build agentic AI systems: agent workflows, tool/function-calling, memory, and human-in-the-loop patterns. Build and productionise RAG pipelines, prompt-based applications, and LLM integrations across providers. Develop and maintain data and ML pipelines: feature engineering, model training, evaluation, and monitoring. Integrate AI systems with enterprise applications (CRMs, ERPs, ITSM tools) via APIs, events, and MCP-based tool servers. Implement guardrails, prompt-injection defences, and evaluation frameworks to keep AI systems safe and reliable in production. 

Write clean, tested, production-grade code and participate actively in code and design reviews. 

Collaborate with architects, product managers, and delivery teams to translate requirements into working AI solutions. Troubleshoot and optimise AI systems for accuracy, latency, and cost in production. 

Required Qualifications 

8–12 years of hands-on software engineering experience, with a strong, unbroken technical track record. Hands-on experience building and shipping AI/ML systems in production, not just POCs. 

Practical experience with agentic AI systems and at least one major agent framework (LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Bedrock Agents/Strands, or Semantic Kernel). 

Experience with LLM/GenAI systems: RAG pipelines, prompt engineering, structured outputs, and tool calling across providers. 

Strong Python skills (TypeScript/Node.js a plus), with production-grade testing, CI/CD, and API design practices. Working knowledge of ML fundamentals: model evaluation, feature engineering, and experimentation. Cloud-native experience on AWS and/or Azure: containers, serverless, event backbones, and vector databases. Understanding of LLM safety and reliability practices: guardrails, prompt-injection defences, and observability. 

About the role

We are building AI systems that read, understand and act on real business documents, bank statements, financial reports, policy documents and forms and putting them into production where accuracy and cost both matters.

This is not a research role and it is not a prompt-writing role. You will own features end to end: pick and deploy open-source models, build the pipelines around them, measure whether they actually work on our documents, drive the cost per document down, and keep the whole thing running in production.

You will work closely with the engineering and product teams, and your work will be directly used by business users from day one.

What you will do

Deploy and evaluate open-source models

• Select, deploy and benchmark open-source LLMs and vision-language models for specific, narrow use cases not general chat.
• Build evaluation sets from real documents and define what "good" means numerically (field-level accuracy, extraction recall, hallucination rate) before shipping.
• Run structured comparisons between models and approaches, and write up the trade-offs so the team can make a decision.
• Apply quantization, batching and other optimizations to fit models into a sensible GPU budget.

Build and optimize AI orchestration

• Design multi-step pipelines that combine deterministic code, ML models and LLM calls and know when not to use an LLM.
• Optimize for latency, cost and reliability: caching, batching, request routing, fallback tiers, retries and graceful degradation.
• Instrument pipelines so failures are visible and traceable rather than silent.

Ship to production

• Package models and services with Docker, expose them behind clean APIs, and deploy them to our GPU and CPU infrastructure.
• Handle the unglamorous production concerns: cold starts, timeouts, concurrency limits, versioning, rollback and monitoring.
• Own on-call-style responsibility for the AI features you build, including cost tracking.

Must-have skills

Programming & engineering

• Strong Python: type hints, async/await, dataclasses/Pydantic, clean module design, testing.
• REST API development with FastAPI (or Flask/Django with a willingness to move to FastAPI).
• Git, code review discipline, and the ability to write code someone else can maintain.
• Comfortable in Linux and on the command line.

Machine learning fundamentals

• Working knowledge of PyTorch and the Hugging Face ecosystem (transformers, tokenizers, accelerate).
• Understanding of inference-time concepts: tokenization, context windows, batching, precision (FP16/BF16/INT8), memory footprint.
• Ability to read a model card and a paper well enough to judge whether a model fits a use case.

Document processing

• Hands-on experience with at least two of: pypdfium2, PyMuPDF, pdfplumber, pdfminer.six, Docling, Unstructured, Surya, DocTR, LayoutLM family.
• Practical OCR experience (Tesseract, PaddleOCR, or a cloud OCR) and an understanding of when OCR is the wrong tool.
• Experience extracting tables from PDFs and dealing with merged cells, multi-line rows, and inconsistent column layouts.

Strongly preferred

You will be a much stronger candidate with any of these. We do not expect all of them.

Model serving & optimization

• vLLM, TGI, Ollama, llama.cpp, or Triton Inference Server.
• Quantization formats and tooling: GGUF, AWQ, GPTQ, bitsandbytes, ONNX Runtime, INT8 export.
• Serverless GPU platforms: Modal, RunPod, Replicate, Baseten including cold-start and container-lifecycle management.
• LoRA / QLoRA fine-tuning with PEFT for narrow, task-specific improvements.

Vision-language models

• Practical use of open VLMs: Qwen2.5-VL, InternVL, Granite Vision, Molmo, Phi-Vision, or similar.
• Awareness of where VLMs hallucinate especially on numeric and financial content and patterns for constraining them (using the model for layout only, sourcing values from the text layer, constrained decoding).

Orchestration & pipelines

• Workflow orchestration: Dagster, Airflow, Prefect, or Temporal.
• Async job patterns: Celery, RQ, or platform-native spawn/poll patterns.
• LLM orchestration frameworks (LangGraph, LlamaIndex, Haystack) with the judgement to know when plain Python is a better answer.
• Structured output enforcement: Instructor, Outlines, XGrammar, JSON schema / tool-use modes.

Evaluation & observability

• Building golden datasets and regression suites for extraction tasks.
• Eval tooling: promptfoo, DeepEval, Ragas, or in-house harnesses.
• LLM tracing and monitoring: Langfuse, Arize Phoenix, LangSmith, OpenTelemetry.

Nice extras

• Rule engines and policy evaluation (Open Policy Agent / Rego, Drools, rule-engine).
• Experience in fintech, lending, insurance or accounting documents.
• Handling of PII and data-security practices in document pipelines.
• Contributions to open-source ML or document-processing projects.

Why join us

• Real production ownership from month one your work goes to actual users, not a demo.
• Genuinely hard technical problems in document AI, not wrappers over an API.
• Small team, short decision cycles, direct access to leadership.
• Budget and freedom to evaluate and adopt new open-source models as they land.

To apply: send your CV along with a short note on one AI system you have taken to production what it did, what the accuracy was, and what broke.

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 Title: AI/ML Consultant

Company Name- WINIT

Location: Hyderabad

Duration- 3 months

Role Overview:

We are looking for a passionate and skilled AI/ML Consultant to join our dynamic team. In this role, you will play a key part in designing and implementing intelligent systems, including the development of a cutting-edge Sales Supervisor Agent. You will work on projects involving Generative AI, Voice AI, sales performance analysis, and recommendation systems that drive automation and strategic decision-making in sales operations. As a consultant, you will collaborate with cross-functional teams to understand business challenges, recommend AI-driven solutions, and deliver scalable, production-ready applications.

Project Knowledge: Generative AI & Voice AI

Experiment with Generative AI models (e.g., GPT, Claude) for tasks such as content creation, email generation, and chat-based assistance.

Build and integrate AI Voice solutions like speech-to-text, call summarization, and conversational agents using tools such as Whisper, ElevenLabs, or Dialogflow.

Integrate GenAI and Voice AI capabilities into the Sales Supervisor Agent for automation and decision support.

[Please refrain from applying if you have over 10 years of experience. This is a hands-on role that requires building from the ground up.]

Location: Bengaluru (In-Office)

Employment Type: Full-Time

About Logikality

Logikality is building an AI-native mortgage intelligence platform for the U.S. mortgage industry. We are reimagining how mortgage operations are executed by combining AI, workflow automation, and domain expertise to solve one of the most document-intensive and decision-heavy industries in the world.

Our platform goes beyond document extraction. We are building AI systems that understand mortgage files, reason across multiple sources of information, identify risks and exceptions, support underwriting and quality control decisions, and continuously improve through expert feedback and rigorous evaluation.

As we expand our AI capabilities, we are looking for a Director, AI Engineering to define and drive the research direction behind our next generation of intelligent systems.

About the Role

This is a hands-on technical leadership role for someone who enjoys solving difficult AI problems and turning research into production impact.

You will lead the research agenda across large language models, reasoning systems, agentic AI, multimodal learning, and intelligent decision support while working closely with engineering, product, and mortgage domain experts. You will prototype new ideas, validate them through rigorous experimentation, and help productionize solutions that directly improve customer outcomes.

This role is ideal for someone with deep research expertise who enjoys building real-world AI systems rather than research for its own sake.

What You'll Do

• Define and execute the Applied AI research roadmap aligned with company and product goals.
• Design novel approaches for document understanding, reasoning, planning, retrieval, and decision support.
• Build agentic AI systems capable of orchestrating tools, workflows, and domain knowledge to solve complex mortgage use cases.
• Develop multimodal AI models that combine documents, structured data, images, and operational context.
• Lead research on long-context reasoning, knowledge integration, memory, retrieval-augmented generation (RAG), and workflow automation.
• Design robust evaluation frameworks, benchmarks, and automated testing pipelines to measure model quality, reliability, explainability, and business impact.
• Rapidly prototype, experiment, and iterate on new AI techniques, evaluating state-of-the-art research for production adoption.
• Work closely with software engineers to translate research prototypes into scalable, production-ready systems.
• Mentor AI engineers and contribute to building a strong research culture within the organisation.
• Collaborate with mortgage domain experts to deeply understand operational workflows, compliance requirements, and decision-making processes.
• Stay current with advances in AI research and identify opportunities to leverage emerging techniques within our platform.
• Represent Logikality in customer interactions, strategic discussions, industry conferences, and business forums, communicating our AI vision, gathering market insights, and helping shape research priorities through direct engagement with customers and ecosystem partners.

What We're Looking For

• PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related discipline; or an engineering degree in Computer Science or related disciplines from a premier engineering institution (e.g., IITs, IISc, NITs, BITS Pilani, or top-tier global universities).
• 3–8 years of professional experience in Applied AI, Machine Learning, or AI Research, with experience building production-grade AI systems
• Strong expertise in modern AI, including Large Language Models, transformers, agentic AI, reasoning systems, retrieval, multimodal learning, or adjacent areas.
• Strong software engineering skills with Python and modern machine learning frameworks.
• Experience designing and implementing production-grade AI systems that solve complex real-world problems.
• Strong understanding of model evaluation, benchmarking, experimentation, and AI system reliability.
• Experience balancing research innovation with engineering pragmatism and product delivery.
• Excellent problem-solving and communication skills with the ability to collaborate across engineering, product, and business teams.

Why Join Logikality?

At Logikality, you'll work on problems that require genuine reasoning, not just text generation. You'll help build AI systems that understand complex documents, synthesise information across workflows, explain decisions, identify exceptions, and improve through continuous learning and expert feedback.

This is an opportunity to work at the intersection of cutting-edge AI research and real-world impact, where your ideas won't remain as papers or prototypes; they'll power intelligent systems used every day by mortgage professionals. We are looking for someone who can connect AI, platform engineering, product thinking and customer outcomes.

For the right person, this could develop into a CTO and co-founder track over the next 6–9 months, based on contribution, technical leadership and mutual fit.

Interested candidates are requested to apply via the Google Form given: https://forms.gle/jFqKzfLhNCcCFU5t9

This will be a full-time in-office role based in Bangalore. Immediate joiners are preferred.

Location: Hyderabad, India. Based at the KnackLabs headquarters, with occasional travel to client locations for workshops and reviews. This role does not involve extended onsite deployments.

About the Role

You will work as an AI Architect who designs the systems behind our client engagements: AI agents, RAG systems, automation platforms, and the conventional backend systems around them.

This is a hands-on design role, not a slideware role. You will scope architectures with clients, make the hard technical decisions, defend them in review, and stay accountable for how the systems perform in production.

You will work directly with clients. Everyone at KnackLabs does. You will sit in design discussions with client engineering teams, present architecture decisions to technical and business stakeholders, and answer for the choices you make.

A full KnackLabs engineering team in Hyderabad builds with you. You own the technical design and the quality of what ships.

What you'll own

1. Architecture - Design AI agents, RAG systems, integrations, and the scalable backend systems around them, for multiple client engagements.
2. Technical scoping - Work directly with clients to turn a business problem into a system design, with clear trade-offs and clear reasons.
3. Scale and reliability - Make sure what we build handles real load: data stores, queues, caching, horizontal scaling, and fault tolerance.
4. Design reviews - Review designs and builds across engagements. Set the technical bar and hold it.
5. Evaluation strategy - Define how we measure accuracy, safety, latency, and cost for the AI systems we ship.
6. Guiding engineers - Raise the level of the engineers building with you, through reviews and direct pairing.
7. Feedback to the platform - Feed what you learn across engagements back into our platform and internal tools.

What we are looking for

1. Around 7 or more years of software engineering experience, including direct work with customers on design or delivery.
2. Full-stack development experience with strength in backend technologies.
3. Experience designing and building scalable applications. You understand how large-scale distributed systems work: data partitioning, queues, caching, horizontal scaling, and fault tolerance.
4. At least 2 years of strong, hands-on AI experience with large language models in production.
5. You build with AI coding tools like Claude Code or Codex as your default way of working. You understand Claude Skills, have written skills yourself, use them actively, and have contributed to them.
6. Hands-on experience building retrieval-augmented generation (RAG) systems: chunking, embeddings, vector databases, retrieval, and reranking.
7. Hands-on experience building AI agents.
8. Strong programming skills in Python. Working knowledge of TypeScript or JavaScript.
9. Experience with at least one cloud platform (AWS, Azure, or GCP).
10. Clear communication. You can explain an architecture decision to an engineer and to a business leader, and defend it under questioning.
11. High ownership and comfort with ambiguity. You can take an unclear problem and turn it into a design.

Nice to have

1. Experience building evaluations to measure accuracy, safety, latency, and cost.
2. Experience with observability and tracing tools such as LangSmith or Braintrust.
3. Experience with on-premises or private cloud (VPC) deployments.
4. Experience deploying AI systems in regulated industries such as insurance, banking, or the public sector.
5. Experience with data engineering and pipelines.
6. A history of side projects, open source contributions, or products you shipped end-to-end.
7. Experience working at a consulting or professional services firm in a client-facing delivery role.

Stack and tools

1. Languages: Python and TypeScript.
2. Models: Claude and other frontier or open-source models, chosen to fit the customer.
3. AI patterns: RAG, agents, prompt engineering, skills, and evaluations.
4. Vector and retrieval: vector databases and retrieval pipelines.
5. Cloud: AWS, Azure, or GCP, on public or private cloud.
6. Integration: REST APIs and enterprise system connectors.

Role Overview:

As an AI Executor/AI Automation Engineer, you will be responsible for designing and integrating AI capabilities into production systems using Python and key ML libraries. This role requires a strong backend development foundation and a proven track record of deploying AI use cases using tools like TensorFlow, Keras, or OpenAI APIs. You'll work cross-functionally to deliver scalable AI-driven solutions.

Key Responsibilities:

• Design and develop backend solutions using Python, with a focus on AI-driven features.
• Implement and integrate AI/ML models using tools like OpenAI, Hugging Face, or Lang Chain.
• Use core Python libraries (NumPy, Pandas, TensorFlow, Keras) to process data, train, or implement models.
• Translate business needs into AI use cases and deliver working solutions.
• Collaborate with product, engineering, and data teams to define integration workflows.
• Develop REST APIs and micro services to deploy AI components within applications.
• Maintain and optimize AI systems for scalability, performance, and reliability.
• Keep pace with advancements in the AI/ML landscape and evaluate tools for continuous improvement.

Required Skills & Qualifications:

• 2+ years of professional experience as an AI/ML Engineer, including strong backend development expertise in Python.
• Proficiency in libraries such as NumPy, Pandas, TensorFlow, and Keras
• Practical exposure to AI platforms/APIs (e.g., OpenAI, LangChain, Hugging Face)
• Solid understanding of REST APIs, micro services, and integration practices
• Ability to work independently in a remote setup with strong communication and ownership
• Excellent problem-solving and debugging capabilities
• Experience with the MERN stack will be an added advantage.