Job highlights

- 8+ years experience with 5+ years in ML/AI engineering, expertise in LangGraph, AutoGen, CrewAI, advanced Python, LLM and prompt engineering, Azure AI stack, vector databases, containerized deployment, and CI/CD
- Build and deploy production-grade GenAI and agentic AI applications, design RAG pipelines, implement responsible AI guardrails, deploy and monitor AI workloads on Azure, define evaluation metrics

At UST, we help the worlds best organizations grow and succeed through transformation. Bringing together the right talent, tools, and ideas, we work with our client to co-create lasting change. Together, with over 30,000 employees in 30+ countries, we build for boundless impact—touching billions of lives in the process. Visit us at UST.com.

Lead AI Engineer

Snapshot

Experience: Overall 8+ years , 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 robust 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