Lead design and deliver enterprise-grade GenAI and agentic LLM solutions including RAG pipelines, retrieval systems, prompt engineering, production APIs, guardrails, evaluation frameworks, and mentor engineering teams while partnering with data engineering, MLOps, and product stakeholders.
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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