Agentic/AI lead/architect with Claude/code/LLM skills1 (India)

Job Description: 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 costlatency 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, planningexecution 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.) - Solid 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 Positive-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 . Responsibilities: 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 costlatency 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 .