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Job Description
Structured overview of role & requirementsAbout This Role
Architect, build, and productionize multi-agent autonomous AI systems using frameworks like LangGraph, AutoGen, and Semantic Kernel on Microsoft Azure.
Develop and integrate LLM-based workflows including RAG pipelines, vector search, and tool/function calling with enterprise APIs for agentic AI solutions.
Lead technical direction and mentorship for GenAI initiatives, establish LLM evaluation frameworks, implement safety/governance guardrails, and own MLOps/LLMOps practices for platform reliability.
Minimum Requirements
8-10 years professional experience in data science/ML, with at least 3 years building LLM or GenAI production applications.
Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or related field.
Hands-on experience with agentic AI frameworks (LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel) and Azure cloud services including Azure OpenAI and Azure ML.
Proficiency in Python (including async programming and API development), strong SQL skills, classical ML knowledge, and experience with MLOps/LLMOps tools and practices.
Ideal Candidate Profile
Experienced technical leader capable of translating complex business problems into scalable agentic AI solutions, plus mentoring junior data scientists.
Strong expertise in LLM-based application patterns, multi-agent orchestration, and cloud-native Azure environments with full ownership of deployment and operational monitoring.
Proven ability to design governance and risk mitigation for LLM systems (hallucination, prompt injection), with hands-on experience in end-to-end GenAI system lifecycle and observability frameworks.