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)