Senior AI Engineer – Agentic AI & RAG

Band: U4 / P1 Experience: 10–12 years Location: Remote (India) Employment Type: Full-Time Role Overview We are looking for a Senior GenAI Engineer with strong hands-on experience in building production-grade Generative AI solutions . The role requires deep expertise in agentic AI architectures, LLM orchestration, and advanced RAG pipelines , with proven experience in deploying scalable, enterprise-grade solutions (not PoCs). Key Responsibilities Design and build end-to-end production-grade GenAI applications using LLMs Develop and orchestrate agentic AI systems (single agent & multi-agent) for complex enterprise workflows Implement RAG pipelines including document ingestion, embeddings, retrieval optimization, and response synthesis Build and optimize LLM orchestration workflows with strong focus on latency, cost, and scalability Implement observability frameworks (tracing, monitoring, logging) for GenAI systems Define and execute evaluation frameworks for LLM response quality, grounding, and hallucination management Develop scalable backend services using Python + FastAPI Build lightweight UI layers using Streamlit for demos/internal tools Ensure production readiness including scalability, resilience, and fault tolerance Collaborate with architecture, data, and platform teams to integrate GenAI into enterprise ecosystems Mandatory Skills (Strict – No Compromise) – REJECT PROFILES IF ANY OF THE BELOW MENTIONED IS MISSING Proven experience in building production-grade GenAI solutions (must demonstrate real deployments) Hands-on expertise in agentic frameworks & orchestration : AWS AgentCore / Strand Agents LangGraph (or equivalent agent orchestration frameworks) Multi-agent system design Strong hands-on experience with RAG architectures : Vector DBs (FAISS, Pinecone, OpenSearch, Chroma, etc.) Embeddings & retrieval strategies (hybrid search, reranking, grounding) Deep understanding of LLM orchestration workflows Experience in LLMOps / Observability / Evaluation : Monitoring LLM performance, tracing, logging Evaluation frameworks (RAGAS, DeepEval or equivalent) Strong coding expertise in Python Experience building APIs using FastAPI Good-to-Have Experience with AWS Bedrock / SageMaker-based GenAI deployments Exposure to guardrails, prompt injection handling, and GenAI risk controls Knowledge of cost optimization & token efficiency strategies Experience in enterprise domains (BFSI, Pharma, Healthcare, Insurance) CI/CD and containerized deployment (Docker/Kubernetes) Profile Expectations (Important for Screening) Must clearly explain architecture of at least 1–2 production GenAI implementations Should demonstrate ownership of solution design (not just usage of APIs/frameworks) Strong depth in agent workflows (planner-executor, tool-calling, multi-agent orchestration) RAG understanding should go beyond chatbot-level (must include retrieval tuning & grounding logic)