Key Responsibilities GenAI & Agentic AI Architecture • Define enterprise reference architectures for Agentic AI and LLM-powered platforms, including: • Single-agent and multi-agent systems • Tool-calling and function orchestration • Memory, planning, and execution layers • Own architectural decisions for Claude / Claude Code and other enterprise-grade LLMs, including model selection, deployment patterns, and cost–latency trade-offs. • Design secure-by-default GenAI systems incorporating: • Guardrails and policy enforcement • Data privacy, PII handling, and prompt safety • Controlled tool execution in regulated environments RAG, Knowledge & Data Systems • Architect large-scale RAG solutions, covering: • Data ingestion and curation pipelines • Chunking and embedding strategies • Vector databases and hybrid search • Evaluation and feedback loops • Partner with Data Engineering teams to ensure data quality, lineage, observability, and governance for AI-driven systems. Platform & Engineering Excellence • Drive production readiness of GenAI systems: • API-first design (FastAPI / REST / event-driven) • CI/CD for LLM workflows • Monitoring, evaluation, and cost tracking • Establish engineering standards, reusable frameworks, and accelerators for faster adoption across EXL accounts. • Review and influence cloud architecture (Azure / AWS / GCP) for scalable and compliant AI deployments. Leadership & Stakeholder Engagement • Act as a technical authority for GenAI across delivery teams and client engagements. • Mentor senior engineers, tech leads, and architects on agentic patterns and advanced LLM engineering. • Partner with clients, product owners, and domain SMEs to shape AI roadmaps, solution designs, and value articulation. Mandatory Skills & Experience 12+ years of total experience with deep hands-on expertise in Generative AI / LLM-based systems, and strong prior background in Data Engineering or Data Science (mandatory). Generative AI / LLM Expertise • Deep hands-on experience with: • Claude / Anthropic ecosystem (including Claude Code exposure is a strong plus) • Other enterprise LLMs (OpenAI, Mistral, LLaMA, etc.) • Strong command over: • Prompt engineering, prompt orchestration, and agent workflows • Tool/function calling, planning–execution loops • LLM and RAG evaluation techniques (precision, grounding, faithfulness) Agentic & RAG Architecture • Proven experience designing: • Agentic AI systems (ReAct, Plan-and-Execute, multi-agent setups) • RAG architectures using vector databases (FAISS, Pinecone, Chroma, etc.) • Strong understanding of hallucination mitigation, guardrails, and safety frameworks. Core Engineering & Platform Skills • Expert-level Python engineering (production-grade systems). • Strong experience with cloud-native AI solutions on Azure, AWS, or GCP. • API design, microservices, and event-driven architectures. Mandatory Prior Background • Data Engineering or Data Science experience is non-negotiable, including: • Data pipelines / ETL / ELT / orchestration • ML or NLP model lifecycle • Analytics platforms or data product engineering Good-to-Have / Preferred • Fine-tuning and adaptation strategies (LoRA / PEFT / prompt tuning). • Experience with MLOps / LLMOps platforms and observability stacks. • Experience delivering GenAI solutions in regulated industries (Insurance, Healthcare, BFS). • Exposure to enterprise AI governance frameworks. Key Responsibilities GenAI & Agentic AI Architecture • Define enterprise reference architectures for Agentic AI and LLM-powered platforms, including: • Single-agent and multi-agent systems • Tool-calling and function orchestration • Memory, planning, and execution layers • Own architectural decisions for Claude / Claude Code and other enterprise-grade LLMs, including model selection, deployment patterns, and cost–latency trade-offs. • Design secure-by-default GenAI systems incorporating: • Guardrails and policy enforcement • Data privacy, PII handling, and prompt safety • Controlled tool execution in regulated environments RAG, Knowledge & Data Systems • Architect large-scale RAG solutions, covering: • Data ingestion and curation pipelines • Chunking and embedding strategies • Vector databases and hybrid search • Evaluation and feedback loops • Partner with Data Engineering teams to ensure data quality, lineage, observability, and governance for AI-driven systems. Platform & Engineering Excellence • Drive production readiness of GenAI systems: • API-first design (FastAPI / REST / event-driven) • CI/CD for LLM workflows • Monitoring, evaluation, and cost tracking • Establish engineering standards, reusable frameworks, and accelerators for faster adoption across EXL accounts. • Review and influence cloud architecture (Azure / AWS / GCP) for scalable and compliant AI deployments. Leadership & Stakeholder Engagement • Act as a technical authority for GenAI across delivery teams and client engagements. • Mentor senior engineers, tech leads, and architects on agentic patterns and advanced LLM engineering. • Partner with clients, product owners, and domain SMEs to shape AI roadmaps, solution designs, and value articulation. Mandatory Skills & Experience 12+ years of total experience with deep hands-on expertise in Generative AI / LLM-based systems, and strong prior background in Data Engineering or Data Science (mandatory). Generative AI / LLM Expertise • Deep hands-on experience with: • Claude / Anthropic ecosystem (including Claude Code exposure is a strong plus) • Other enterprise LLMs (OpenAI, Mistral, LLaMA, etc.) • Strong command over: • Prompt engineering, prompt orchestration, and agent workflows • Tool/function calling, planning–execution loops • LLM and RAG evaluation techniques (precision, grounding, faithfulness) Agentic & RAG Architecture • Proven experience designing: • Agentic AI systems (ReAct, Plan-and-Execute, multi-agent setups) • RAG architectures using vector databases (FAISS, Pinecone, Chroma, etc.) • Strong understanding of hallucination mitigation, guardrails, and safety frameworks. Core Engineering & Platform Skills • Expert-level Python engineering (production-grade systems). • Strong experience with cloud-native AI solutions on Azure, AWS, or GCP. • API design, microservices, and event-driven architectures. Mandatory Prior Background • Data Engineering or Data Science experience is non-negotiable, including: • Data pipelines / ETL / ELT / orchestration • ML or NLP model lifecycle • Analytics platforms or data product engineering Good-to-Have / Preferred • Fine-tuning and adaptation strategies (LoRA / PEFT / prompt tuning). • Experience with MLOps / LLMOps platforms and observability stacks. • Experience delivering GenAI solutions in regulated industries (Insurance, Healthcare, BFS). • Exposure to enterprise AI governance frameworks. Key Responsibilities GenAI & Agentic AI Architecture • Define enterprise reference architectures for Agentic AI and LLM-powered platforms, including: • Single-agent and multi-agent systems • Tool-calling and function orchestration • Memory, planning, and execution layers • Own architectural decisions for Claude / Claude Code and other enterprise-grade LLMs, including model selection, deployment patterns, and cost–latency trade-offs. • Design secure-by-default GenAI systems incorporating: • Guardrails and policy enforcement • Data privacy, PII handling, and prompt safety • Controlled tool execution in regulated environments RAG, Knowledge & Data Systems • Architect large-scale RAG solutions, covering: • Data ingestion and curation pipelines • Chunking and embedding strategies • Vector databases and hybrid search • Evaluation and feedback loops • Partner with Data Engineering teams to ensure data quality, lineage, observability, and governance for AI-driven systems. Platform & Engineering Excellence • Drive production readiness of GenAI systems: • API-first design (FastAPI / REST / event-driven) • CI/CD for LLM workflows • Monitoring, evaluation, and cost tracking • Establish engineering standards, reusable frameworks, and accelerators for faster adoption across EXL accounts. • Review and influence cloud architecture (Azure / AWS / GCP) for scalable and compliant AI deployments. Leadership & Stakeholder Engagement • Act as a technical authority for GenAI across delivery teams and client engagements. • Mentor senior engineers, tech leads, and architects on agentic patterns and advanced LLM engineering. • Partner with clients, product owners, and domain SMEs to shape AI roadmaps, solution designs, and value articulation. Mandatory Skills & Experience 12+ years of total experience with deep hands-on expertise in Generative AI / LLM-based systems, and strong prior background in Data Engineering or Data Science (mandatory). Generative AI / LLM Expertise • Deep hands-on experience with: • Claude / Anthropic ecosystem (including Claude Code exposure is a strong plus) • Other enterprise LLMs (OpenAI, Mistral, LLaMA, etc.) • Strong command over: • Prompt engineering, prompt orchestration, and agent workflows • Tool/function calling, planning–execution loops • LLM and RAG evaluation techniques (precision, grounding, faithfulness) Agentic & RAG Architecture • Proven experience designing: • Agentic AI systems (ReAct, Plan-and-Execute, multi-agent setups) • RAG architectures using vector databases (FAISS, Pinecone, Chroma, etc.) • Strong understanding of hallucination mitigation, guardrails, and safety frameworks. Core Engineering & Platform Skills • Expert-level Python engineering (production-grade systems). • Strong experience with cloud-native AI solutions on Azure, AWS, or GCP. • API design, microservices, and event-driven architectures. Mandatory Prior Background • Data Engineering or Data Science experience is non-negotiable, including: • Data pipelines / ETL / ELT / orchestration • ML or NLP model lifecycle • Analytics platforms or data product engineering Good-to-Have / Preferred • Fine-tuning and adaptation strategies (LoRA / PEFT / prompt tuning). • Experience with MLOps / LLMOps platforms and observability stacks. • Experience delivering GenAI solutions in regulated industries (Insurance, Healthcare, BFS). • Exposure to enterprise AI governance frameworks.