Senior AI Engineer – GenAI & Agentic AI

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.