Lead AI Data Engineer

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)

Responsibilities: 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)

Qualifications: 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)