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Lead AI Engineer

Snapshot

Experience: over all 8+ years and 5+ years in ML/AI engineering

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 solid 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.

📌 Data Scientist (Bengaluru)
🏢 UST
📍 Bengaluru