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Lead AI Data Engineer

EXL

Noida

9-12 years

Today

$73.5K–92.8K/yr

Full time

Onsite

Skills Required LLM

RAG

Agentic AI

Gen AI

Embeddings

Prompt Engineering

LangChain

OpenAI

Claude

Azure OpenAI vector indexing tool-calling function-calling

Python

PySpark

Description Lead GenAI engineering and solution architecture for enterprise-grade agentic and LLM solutions. The role focuses on RAG, retrieval optimisation, productionisation, governance, and cross-functional delivery leadership.

Company: EXL

Role: Lead AI Data Engineer

Location: Noida

Experience

- 9–12 years total experience
- 2–4+ years hands-on in LLM / GenAI delivery
- Experience leading solution design or small teams

Responsibilities

- Architect enterprise-grade agentic and LLM solutions
- Define scalable GenAI system design patterns
- Act as the technical anchor for GenAI initiatives
- Drive design reviews, architecture governance, and best practices
- Design and build agentic systems for knowledge assistants, document automation and workflow orchestration
- Build tool-calling and function-calling frameworks for agent workflows
- Lead end-to-end RAG implementation from ingestion to response generation
- Optimise retrieval quality across recall, relevance, and grounding
- Evaluate and benchmark different architectures
- Develop production-grade APIs and services
- Drive code quality, testing standards, and reusable architecture components
- Ensure performance-optimised solutions across latency, cost, and reliability
- Partner with Data Engineering, MLOps, and Business/Product teams
- Drive end-to-end delivery ownership across multiple projects
- Mentor and guide junior engineers and project teams
- Conduct technical reviews, solution walkthroughs, and code reviews
- Support pre-sales, RFPs, and solution proposals with architecture inputs
- Drive reusable accelerators, frameworks, and COE assets
- Help shape EXL’s GenAI strategy
- Influence technology choice,



design decisions, and roadmap planning

Additional Responsibilities

- Implement advanced prompt engineering strategies
- Implement LLM guardrails for hallucination control, safety filters, and policy enforcement
- Define evaluation frameworks for response quality metrics, RAG benchmarking, and human-in-the-loop validation
- Stay ahead of industry evolution

Nice To Have

- Fine-tuning approaches: LoRA, PEFT, prompt tuning
- Experience with Azure AI stack
- Azure OpenAI
- AI Search
- Enterprise security and data privacy in GenAI
- Coding agents
- Autonomous agent frameworks
- Experience in insurance or BFSI domains

More Skills retrieval optimisation, GPT, LangGraph, agent orchestration, tool-calling architectures, prompt orchestration, reasoning chains, API integration, deep data analysis, large volume of data, Fabric, Azure Databricks, Snowflake, Azure, AWS, GCP, SQL, containers, CI/CD, monitoring, ETL, ELT, pipelines, orchestration, NLP, analytics engineering, data products, FastAPI, Flask, LLM guardrails, hallucination control, safety filters, policy enforcement, response quality metrics, RAG benchmarking, human-in-the-loop validation

Prepare for this role

Recommended resources to build the skills for this position. Sponsored.

Top 15 Embeddings Interview Questions (Quick)

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Curated LangChain interview questions covering chains, agents, and memory.

Top 50 RAG Interview Questions

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Curated RAG interview questions and answers from Zenaique.

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