Lead AI Data Engineer

ExlService Holdings, Inc.; Dadri; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

EXL Service Holdings, Inc. in India invites a senior solutions architect to lead enterprise-grade agentic and LLM-based systems. You will shape scalable GenAI design patterns, mentor engineers, and own end-to-end delivery across multiple projects.

You will work with data engineering, MLOps, and product teams to build production APIs, implement guardrails, and optimize performance and cost. A strong background in Python, PySpark, LangChain, and cloud platforms is essential.

Qualifications

• 9–12 years total experience.
• 2–4+ years hands-on in LLM / GenAI delivery (production use cases).
• Strong hands-on experience with LLMs (Claude, OpenAI, etc.).
• Experience with LangChain, LangGraph, or similar frameworks.
• Agent orchestration and tool-calling architectures.
• Deep understanding of LLM limitations, evaluation, and optimisation strategies.
• Strong Python/PySpark production-grade development and API integration experience.
• Data engineering and NLP lifecycle experience.

Responsibilities

• Architect enterprise-grade agentic and LLM solutions for multiple projects.
• Define scalable GenAI system design patterns and governance.
• Act as technical anchor for GenAI initiatives across projects.
• Lead design reviews, architecture governance, and best practices.
• Design and build agentic systems, prompt strategies, and tool-calling frameworks.
• Develop production-grade APIs/services with performance focus.
• Mentor junior engineers and drive reusable accelerators and COE assets.
• Collaborate with Data Engineering, MLOps, and Product teams for end-to-end delivery.
• Shape EXL's GenAI strategy and guide technology choices.

Skills

LLMs

RAG pipelines

Agentic AI

LangChain

Agent orchestration

Tool calling

LLM evaluation

Python

PySpark

API integration

Data engineering

Databricks

Snowflake

Cloud platforms

Azure

AWS

GCP

SQL

Containers

CI/CD

Monitoring

Data Science

NLP

LoRA

PEFT

Prompt tuning

Azure OpenAI

AI security

Tools

Fabric

Azure Databricks

Snowflake

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)