Lead AI Data Engineer

EXL Talent Acquisition Team; Dadri; On-site; INR 3,800,000 - 6,200,000; Full time

Job summary

EXL is seeking a Senior GenAI Architect to lead solution architecture and technical leadership for enterprise agentic AI initiatives. You will define scalable GenAI patterns and drive architecture reviews.

Collaborating with Data Engineering, MLOps, and Product teams, you’ll deliver end-to-end solutions, mentor engineers, and help shape EXL’s GenAI strategy while improving performance, reliability and cost efficiency across deployments.

Qualifications

• Experience with LLM deployments, RAG pipelines and prompt strategies.
• Strong Python/PySpark production-grade API development.
• Exposure to LangChain/LangGraph and tooling for agentic workflows.
• Leadership in solution design and cross-team collaboration.

Responsibilities

• Lead solution architecture for enterprise GenAI initiatives with governance.
• Design scalable GenAI systems, orchestration, and evaluation frameworks.
• Mentor junior engineers and drive end-to-end delivery across projects.
• Collaborate with Data Engineering, MLOps, and Product teams to align use-cases.

Skills

LLM development

Agent orchestration

Python

Spark/PySpark

Databricks/Azure

Cloud platforms

Data analysis

Stakeholder comms

Leadership

Tools

LangChain

LangGraph

Tool-calling

Azure OpenAI

Job description

Key Responsibilities

1. 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

2. 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

3. 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

4. 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)

5. 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

6. 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

7. 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)