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)