Lead AI Data Engineer
Hybrid Gurugram Noida Full-time Senior Data Engineering
At a glance
Senior Data Engineering role at Fa Ewjt Saasfaprod1. Gurugram · full-time.
Pay not stated Data Engineering salary
RoleData Engineering
SenioritySenior
LocationGurugram
WorkplaceHybrid
EmploymentFull-time
PostedJun 25, 2026
Also hiring inNoida
Role brief
dataroles summary, based on the employer's posting.
What you'll do
- Shape architecture for agentic AI and LLM projects, setting patterns for retrieval, orchestration and evaluation
- Build LLM agents for knowledge support, document processing and workflow tasks, including tool-calling capabilities
- Lead retrieval-augmented pipelines from data preparation through answer generation, then assess retrieval quality
- Deliver production APIs and reusable components, with attention to testing, speed, cost and reliability
- Set safeguards and evaluation methods, coordinate delivery across teams, and mentor engineers through design and code reviews
What you bring
- At least nine years in total, including two or more years delivering LLM or GenAI solutions in production
- Strong Python or PySpark skills, API integration experience, and hands-on work with LLMs, agent systems and retrieval pipelines
- Experience with LangChain, LangGraph or comparable frameworks, plus agent coordination and tool-calling designs
- Background in data engineering, ML or data science, or analytics engineering, alongside experience leading designs or small teams
Who this fits
This senior role suits someone who can lead technical design while still building production GenAI systems. You’ll guide junior engineers and work across data engineering, MLOps and business or product teams. The position is based in Gurugram and is hybrid.
From the employer
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)
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