Senior Supply Chain AI & Analytics Engineer

AMD AMD · Semiconductors · Austin, TX · Engineering

Senior engineer to architect, develop, and operationalize AI-driven analytics solutions for AMD's supply chain, focusing on planning, logistics, and inventory. The role involves building AI models and agents, integrating them with enterprise platforms, and enabling autonomous decision-making within the supply chain. Requires strong technical depth, architectural thinking, and collaboration skills.

What you'd actually do

  1. Design, build, and deploy AI/ML models and advanced analytics that improve planning, inventory, logistics, network optimization, and S&OP, while architecting scalable data and analytics solutions that integrate seamlessly with enterprise platforms and supply chain systems.
  2. Develop and operationalize AI agents that continuously monitor supply chain signals, surface proactive insights, recommend actions, and automate decision workflows to enable a more autonomous supply chain environment.
  3. Translate ambiguous and complex supply chain problems into structured, data-driven solutions by performing exploratory analysis, scenario modeling, root‑cause investigations, and optimization across large datasets.
  4. Create high‑quality dashboards, semantic models, and KPI frameworks in Power BI; enable search‑driven and natural‑language analytics through ThoughtSpot; and establish best practices for visualization and data storytelling.
  5. Partner closely with data engineering teams to ensure reliable, well‑modeled datasets, while supporting pipeline development, feature engineering, and model deployment to production with an emphasis on scalability, explainability, and maintainability.

Skills

Required

  • Python and/or R
  • building and deploying AI/ML models
  • generative solutions
  • AI agents
  • Power BI
  • ThoughtSpot
  • architect scalable data and analytics solutions
  • integrating models, pipelines, and AI agents with enterprise platforms
  • planning, logistics, inventory, network optimization, S&OP
  • feature engineering
  • production pipelines
  • deploying models/agents into repeatable, maintainable operating environments
  • optimization techniques
  • LLMs
  • prompt engineering
  • enterprise agent frameworks
  • explainable and user-friendly solutions
  • global teams
  • manage shifting priorities
  • collaborate across functions
  • communicate clearly with leadership and technical partners
  • Bachelor’s or master’s degree in data science, Engineering, Operations Research, Supply Chain, Computer Science, or related field

Nice to have

  • semiconductor supply chain
  • SAP
  • people leadership

What the JD emphasized

  • AI/ML models
  • AI agents
  • operationalize AI agents
  • deploy AI/ML models
  • deploying models/agents into repeatable, maintainable operating environments
  • enterprise agent frameworks

Other signals

  • architecting scalable data and analytics solutions
  • Develop and operationalize AI agents
  • integrate models, pipelines, and AI agents with enterprise platforms
  • deploying models/agents into repeatable, maintainable operating environments