Lead AI Data Engineer

EXL Talent Acquisition Team
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📍 Noida, Uttar Pradesh, India

About this role

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)

Frequently Asked Questions

Is the salary disclosed for the Lead AI Data Engineer position at EXL Talent Acquisition Team?
The salary for this Lead AI Data Engineer role at EXL Talent Acquisition Team is not publicly listed. Click "Apply Now" to learn more about the compensation package on their official careers page.
Where is the Lead AI Data Engineer position at EXL Talent Acquisition Team located?
This Lead AI Data Engineer role at EXL Talent Acquisition Team is based in Noida, Uttar Pradesh, India. The position is listed as on-site or hybrid. Check the full job description or apply directly to confirm the work arrangement.
How do I apply for the Lead AI Data Engineer position at EXL Talent Acquisition Team?
Click the "Apply Now" button on this page. You will be redirected to EXL Talent Acquisition Team's official application portal hosted on oraclecloud where you can submit your application directly.
When was the Lead AI Data Engineer job at EXL Talent Acquisition Team posted?
This Lead AI Data Engineer position at EXL Talent Acquisition Team was posted on Jul 4, 2026. Apply as soon as possible — early applications are often reviewed first.
Lead AI Data Engineer
EXL Talent Acquisition Team
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