Physical AI Senior Manager

Senior Manager role focused on Physical AI strategy and implementation within manufacturing and supply chain operations. Responsibilities include leading client engagements, driving proofs of concept, and overseeing end-to-end solution architecture involving robotics, computer vision, simulation, digital twins, synthetic data, and edge AI. The role requires integrating AI with real-world operational constraints and leading multi-disciplinary teams.

What you'd actually do

  1. Lead Physical AI strategy and advisory for manufacturing and supply chain clients. Identify high-value use cases (e.g., quality inspection, safety, intralogistics, material handling, asset monitoring, autonomous operations), define value hypotheses, and translate to roadmaps and business cases.
  2. Own solution shaping and end-to-end architecture spanning sensors, vision, data pipelines, model development, simulation, edge deployment, and operations (i.e., MLOps and ModelOps), with explicit acceptance criteria for operational environments.
  3. Drive PoCs and pilots to measurable outcomes. Define experiments, data collection plans, synthetic data approaches (when appropriate), evaluation metrics, and scale plans from pilot-to-plant and factory/network rollout.
  4. Integrate AI with real-world constraints: latency, reliability, safety, OT/IT connectivity, cybersecurity, model drift, human-in-the-loop workflows, and maintenance/operating model considerations.
  5. Lead and mentor multi-disciplinary teams: data science, ML engineering, software and edge, vision, robotics and controls, manufacturing experts, and contribute to capability-building and market activation.

Skills

Required

  • Client leadership
  • Team leadership
  • Business development/pursuits
  • Manufacturing and supply chain domain experience
  • Robotics
  • Computer vision
  • Simulation
  • Digital twins
  • Synthetic data
  • Edge AI
  • MLOps
  • ModelOps
  • OT/IT integration
  • Cybersecurity
  • Human-in-the-loop systems

Nice to have

  • Strategy advisory
  • Solution architecture
  • Proof of concept development
  • Pilot execution
  • Alliance partnership management

What the JD emphasized

  • Physical AI
  • manufacturing and supply chain
  • robotics
  • computer vision
  • simulation
  • digital twins
  • synthetic data
  • edge AI
  • MLOps
  • ModelOps
  • real-world constraints
  • latency
  • reliability
  • safety
  • OT/IT connectivity
  • cybersecurity
  • model drift
  • human-in-the-loop workflows

Other signals

  • Physical AI strategy and advisory
  • Implementations combining robotics, computer vision, simulation, digital twins, synthetic data, and edge AI
  • MLOps and ModelOps
  • Integrate AI with real-world constraints: latency, reliability, safety, OT/IT connectivity, cybersecurity, model drift, human-in-the-loop workflows