AI Engineer + Data Scientist- Lead
UST (company site, opens in a new tab)5+ yrsBengaluruOn-siteDirect career pageEst. 22–38 LPA
Posted 17h ago
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Job description
Structured overview of role & requirements
About This Role
- Build and deploy production-grade GenAI and agentic AI applications automating enterprise workflows, managing end-to-end delivery from design through deployment.
- Design retrieval-augmented generation (RAG) pipelines including chunking, hybrid search, re-ranking, memory, and tool orchestration.
- Deploy and monitor AI workloads on Azure (AKS/ARO) leveraging CI/CD, observability tools, and implement responsible AI guardrails including prompt-injection defense and content filtering.
Minimum Requirements
- Minimum 5 years of ML/AI engineering experience with production-level LLM/agentic application delivery.
- Advanced proficiency in Python and experience with at least one agent framework (LangGraph, AutoGen, CrewAI, or PydanticAI).
- Hands-on experience with Azure AI stack (Azure OpenAI, AI Search, AI Services) and containerized deployment (Docker/Kubernetes, AKS/ARO).
- Experience with vector databases (FAISS, Pinecone, Chroma), prompt engineering for LLMs (GPT, Claude, LLaMA), and CI/CD tools (Jenkins, GitHub Actions).
Ideal Candidate Profile
- Proven track record of delivering 3+ production AI deployments with measurable business impact, demonstrating ownership of AI solution lifecycle.
- Experienced in complex RAG pipeline design and deployment in cloud environments, especially Azure Kubernetes services with observability and responsible AI features.
- Hands-on builder comfortable with deep technical integrations in AI workflows including vector search, prompt engineering, and agent frameworks.