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:
o AWS AgentCore / Strand Agents
o LangGraph (or equivalent agent orchestration frameworks)
o Multi-agent system design
Strong hands-on experience with RAG architectures:
o Vector DBs (FAISS, Pinecone, OpenSearch, Chroma, etc.)
o Embeddings & retrieval strategies (hybrid search, reranking,
grounding)
Deep understanding of LLM orchestration workflows
Experience in LLMOps / Observability / Evaluation:
o Monitoring LLM performance, tracing, logging
o 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)
Pay: ₹550,000.00 - ₹900,000.00 per year
Work Location: Remote
