Lead AI Data Engineer

Key Responsibilities

• Solution Architecture & Technical Leadership
• Architect enterprise-grade agentic and LLM solutions (single-agent, multi-agent, tool-driven workflows)
• Define scalable GenAI system design patterns (RAG, orchestration layers, evaluation frameworks)
• Act as the technical anchor for GenAI initiatives across projects
• Drive design reviews, architecture governance, and best practices
• Agentic AI & LLM Engineering
• Design and build agentic systems using LLMs for use cases such as: - Knowledge assistants
• Document automation & intelligence
• Workflow orchestration
• Implement advanced prompt engineering strategies, prompt orchestration, and reasoning chains
• Build tool-calling / function-calling frameworks for agent workflows
• RAG & Retrieval Systems
• Lead end-to-end implementation of RAG pipelines: - Data ingestion → chunking → embeddings → vector indexing → retrieval → response generation
• Optimise retrieval quality (recall, relevance, grounding)
• Evaluate and benchmark different architectures
• Productisation & Engineering Excellence
• Develop production-grade APIs/services (FastAPI, Flask, etc.)
• Drive code quality, testing standards, and reusable architecture components
• Ensure solutions are performance optimised (latency, cost, reliability)
• Governance, Safety & Evaluation
• Implement LLM guardrails: - Hallucination control
• Safety filters
• Policy enforcement
• Define evaluation frameworks: - Response quality metrics
• RAG benchmarking
• Human-in-the-loop validation
• Collaboration & Delivery Leadership
• Partner with: - Data Engineering → pipelines, data quality, governance
• MLOps → deployment, CI/CD, monitoring
• Business/Product → use-case alignment
• Drive end-to-end delivery ownership across multiple projects
• Technical Leadership Responsibilities (Critical Addition)
• Mentor and guide junior engineers and project teams
• Conduct technical reviews, solution walkthroughs, and code reviews
• Support pre-sales / RFPs / solution proposals with architecture inputs
• Drive reusable accelerators, frameworks, and COE assets
• Stay ahead of industry evolution and help shape EXL’s GenAI strategy
• Influence technology choice, design decisions, and roadmap planning Must-Have Skills

Experience

• 9–12 years total experience
• 2–4+ years hands-on in LLM / GenAI delivery (production use cases) LLM / GenAI & Agentic Engineering
• Strong hands-on experience with: - LLMs (Claude, OpenAI, etc.)
• RAG pipelines and retrieval optimisation
• GPT + Agentic AI implementation experience
• Experience with: - LangChain, LangGraph, or similar frameworks
• Agent orchestration and tool-calling architectures
• Deep understanding of: - LLM limitations, evaluation, and optimisation strategies Core Engineering
• Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience
• Deep data analysis experience and handling large volume of data
• Fabric/Azure Databricks/Snowflake data engineering integration skills
• Good exposure to: - Cloud platforms (Azure/AWS/GCP)
• SQL
• Containers, CI/CD, monitoring Data / AI Foundations (Mandatory)

Prior Experience In One Or More

• Data Engineering (ETL/ELT, pipelines, orchestration)
• Data Science / ML lifecycle (especially NLP)
• Analytics engineering / data products Leadership Capabilities
• Experience leading solution design or small teams
• Ability to translate business problems into AI solutions
• Strong stakeholder communication and influencing skills

Good-to-Have / Preferred

• Fine-tuning approaches: LoRA / PEFT / prompt tuning
• Experience with Azure AI stack (Azure OpenAI, AI Search)
• Exposure to: - Enterprise security & data privacy in GenAI
• Coding agents / autonomous agent frameworks
• Experience in insurance / BFSI domains (valuable for EXL use cases)