Lead AI Data Engineer

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)

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)

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)