Posted:6 days ago
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 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.
Free courses, interview questions and practice for each skill.
Hybrid
Full Time
UST
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