Senior AI Engineer – GenAI & Agentic AI

Location : ITPL, Bengaluru
work mode : 4 days work from office
Interview: AMAT interview will face to face - need local candidates
Experience Required: 5–8 years
Salary : 25 LPA


Open New Demand # AMDIA00244 - AI Engineer + Data Scientist - 2
AI Engineer / Developer
Snapshot
Experience: 5–7 years in ML/AI engineering
Reports To: AI Technical Lead / Manager, AIML
Education: B.E./B.Tech or M.Tech in CS, Data Science, or related field
About the Role
Build and ship production-grade GenAI and agentic AI applications that automate
enterprise workflows — from design through deployment. A hands-on individual-
contributor role for a strong builder.
Key Responsibilities
• Build agentic applications using LangGraph, AutoGen, CrewAI, or Semantic Kernel.
• Design RAG pipelines — chunking, hybrid search, re-ranking, memory, and tool
orchestration.
• Deploy and monitor AI workloads on Azure (AKS/ARO) with CI/CD and observability.
• Implement responsible-AI guardrails — prompt-injection defense and content filtering.
• Define evaluation metrics for task success, hallucination, latency, and cost.
Must-Have Skills
• 5+ years ML/AI engineering with production LLM/agentic delivery.

Open Demands JD

• Advanced Python and at least one agent framework (LangGraph, AutoGen, CrewAI,
PydanticAI).
• Strong LLM and prompt-engineering skills (GPT, Claude, LLaMA); hands-on RAG
workflows.
• Azure AI stack (Azure OpenAI, AI Search, AI Services) and Databricks ML (MLflow,
Delta Lake).
• Vector databases (FAISS, Pinecone, Chroma) and embedding/retrieval design.
• Containerized deployment (Docker/Kubernetes, AKS/ARO) and REST APIs /
WebSockets / event-driven services.
• CI/CD and version control (Jenkins / GitHub Actions, Git) with SDLC and agile
practices.
• Portfolio of 3+ production AI deployments with measurable business impact.
Nice to Have
• Model fine-tuning (LoRA/PEFT), multi-modal AI, and model evaluation frameworks.
• LLMOps / MLOps and model monitoring (drift, latency, cost, hallucination).
• Knowledge graphs (Neo4j); AWS Bedrock / GCP Vertex AI exposure.
• AI-augmented dev tools (GitHub Copilot, Claude Code, Windsurf) for rapid prototyping.
• Enterprise AI security, compliance, and governance awareness.
• Manufacturing or supply-chain domain experience.