Lead AI Data Engineer

EXL seeks a Lead AI Data Engineer in Gurugram, Haryana, India, to provide hands-on technical leadership for enterprise-grade LLM and agentic AI solutions. The role spans architecture, implementation, governance and delivery across multiple projects.

Responsibilities

• Architect single- and multi-agent systems and tool-driven workflows for knowledge assistants, document automation and workflow orchestration. Establish scalable GenAI design patterns, lead design reviews and guide architecture decisions.
• Build prompt orchestration, reasoning chains and tool- or function-calling frameworks. Lead end-to-end RAG pipelines covering ingestion, chunking, embeddings, vector indexing, retrieval and response generation; improve recall, relevance and grounding, and benchmark architectures.
• Develop production-grade APIs and services, reusable components and testing standards. Optimize latency, cost and reliability. Implement hallucination controls, safety filters and policy enforcement, with response-quality metrics, RAG benchmarking and human-in-the-loop validation.
• Coordinate with data engineering on pipelines, quality and governance; with MLOps on deployment, CI/CD and monitoring; and with business and product teams on use cases. Own delivery, mentor engineers, conduct technical and code reviews, contribute architecture input to pre-sales proposals, and help shape technology choices and the GenAI roadmap.

Requirements

• 9–12 years of total experience, including 2–4+ years of hands-on LLM/GenAI delivery for production use cases. Experience with LLMs such as Claude or OpenAI, RAG, GPT-based agentic implementations, LangChain or LangGraph, agent orchestration, and LLM limitations, evaluation and optimization.
• Production-grade Python and PySpark engineering, API integration, large-volume data analysis, and data engineering integration using Fabric, Azure Databricks or Snowflake. Exposure to Azure, AWS or GCP, SQL, containers, CI/CD and monitoring. Prior experience in at least one of data engineering, the data science/ML lifecycle—especially NLP—or analytics engineering/data products is mandatory.
• Experience leading solution design or small teams, translating business problems into AI solutions, and communicating with stakeholders.

Preferred

LoRA, PEFT or prompt tuning; Azure OpenAI and AI Search; enterprise security and GenAI data privacy; coding or autonomous agents; and insurance or BFSI domain experience.