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Job Description
Structured overview of role & requirementsAbout This Role
Lead design, development, and production deployment of multi-agent autonomous AI systems and agentic workflows using frameworks like LangGraph, AutoGen, and Semantic Kernel.
Own end-to-end Retrieval-Augmented Generation (RAG) pipelines and integration of intelligent agents with enterprise systems, ensuring performance, safety, and governance.
Mentor junior team members and set technical direction for enterprise-wide GenAI initiatives, translating ambiguous business problems into scalable AI solutions.
Minimum Requirements
8-10 years professional experience in data science/ML with at least 3 years in production LLM/GenAI application development.
Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or related field.
Hands-on expertise in agentic frameworks (LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel) and advanced Python programming (async, FastAPI).
Proven experience with Microsoft Azure AI services (Azure OpenAI, Azure ML, Azure AI Search, Databricks, Synapse) and solid classical ML knowledge with TensorFlow or PyTorch.
Ideal Candidate Profile
Experienced leader capable of architecting complex multi-agent AI systems extending across cloud and enterprise environments with strong operational ownership.
Deep practical knowledge of LLM application patterns including prompt engineering, function/tool calling, and agent orchestration at scale.
Skilled in MLOps/LLMOps practices, with ability to build evaluation frameworks, implement safety guardrails, and optimize production AI workloads on Azure.