Staff Data Scientist, Agentic AI (SMAI)

About the role

Micron is looking for a highly motivated Agentic AI Data Scientist to lead the design and deployment of next-generation AI agents within SMAI (Smart Manufacturing & AI). The role builds goal-driven, multi-step AI systems that autonomously plan, decide and execute workflows across manufacturing planning, capacity optimization and operations intelligence, driving cycle time reduction, capacity improvements and decision automation.

Key responsibilities

• Design and develop agent-based AI systems with multi-step reasoning, autonomous orchestration and multi-agent architectures, integrating optimization models and LLM-based reasoning and tool use.
• Apply Agentic AI to capacity planning, capital investment optimization, production flow optimization and scenario simulation, turning business needs into structured optimization problems and AI-driven decision workflows.
• Integrate agents into existing SMAI platforms, data pipelines and enterprise systems, and build reusable frameworks for agent orchestration and knowledge retrieval (RAG / knowledge graphs).
• Drive deployment strategy (embedded vs standalone agents) and collaborate with planning, operations, manufacturing, data engineering, MLOps and platform teams, communicating technical solutions to business stakeholders.

Required profile

• Bachelor's or Master's degree in Computer Science, Data Science, Industrial Engineering, Operations Research or a related field.
• 3-5+ years of experience in AI/ML engineering, optimization / decision science, or advanced analytics in operations and planning.
• Proven experience building production-grade AI or optimization solutions.
• Preferred: PhD in AI, ML or Operations Research, and experience in semiconductor or advanced manufacturing environments.

Required skills

• Strong programming in Python (preferred) and modern AI frameworks.
• LLMs / GenAI, prompt engineering, RAG and multi-agent systems.
• Operations Research and optimization: linear / mixed integer programming, heuristics, simulation models.
• Structured and unstructured data, APIs, data pipelines (Spark, SQL) and MLOps.
• Preferred: agent frameworks (LangChain, AutoGen, CrewAI), reinforcement learning, knowledge graphs.