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 Gen AI 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 Po Cs).
Key Responsibilities
Design and build end-to-end production-grade Gen AI 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 Gen AI systems
Define and execute evaluation frameworks for LLM response quality, grounding, and hallucination management
Develop scalable backend services using Python + Fast API
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 Gen AI into enterprise ecosystems
Mandatory Skills (Strict – No Compromise) – REJECT PROFILES IF ANY OF THE BELOW MENTIONED IS MISSING
Proven experience in building production-grade Gen AI solutions (must demonstrate real deployments)
Hands-on expertise in agentic frameworks & orchestration :
AWS Agent Core / Strand Agents
Lang Graph (or equivalent agent orchestration frameworks)
Multi-agent system design
Strong hands-on experience with RAG architectures :
Vector DBs (FAISS, Pinecone, Open Search, 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, Deep Eval or equivalent)
Strong coding expertise in Python
Experience building APIs using Fast API
Good-to-Have
Experience with AWS Bedrock / Sage Maker-based Gen AI deployments
Exposure to guardrails, prompt injection handling, and Gen AI 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 Gen AI 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)