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About the role
Lead AI Data Engineer
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Job Info
- Job Identification15576
- Posting Date06/25/2026, 09:53 AM
- Job RoleGen AI Engineer-Gen AI Engineer
- Experience (In Years)9-12
- Job LocationGurgaon
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)
Required Skills
- leader
About Us
EXL (NASDAQ: EXLS) is a leading data analytics and digital operations and solutions company. We partner with clients using a data and AI-led approach to reinvent business models, drive better business outcomes and unlock growth with speed. EXL harnesses the power of data, analytics, AI, and deep industry knowledge to transform operations for the world’s leading corporations in industries including insurance, healthcare, banking and financial services, media and retail, among others. EXL was founded in 1999 with the core values of innovation, collaboration, excellence, integrity and respect. We are headquartered in New York and have more than 54,000 employees spanning six continents. For more information, visit www.exlservice.com.
EXL never requires or asks for fees/payments or credit card or bank details during any phase of the recruitment or hiring process and has not authorized any agencies or partners to collect any fee or payment from prospective candidates. EXL will only extend a job offer after a candidate has gone through a formal interview process with members of EXL’s Human Resources team, as well as our hiring managers.
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