AI engineer/Developer

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.

• 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.