Senior AI Engineer – Agentic AI & RAG

Job description

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)