Director Embedded AI Engineering

Honeywell Honeywell · Industrial · Atlanta, GA +1

Lead hands-on development and deployment of Edge AI solutions, focusing on model optimization and MLOps pipelines for embedded platforms with GPUs/AI accelerators.

What you'd actually do

  1. Lead hands-on development and deployment of Edge AI solutions with a focus on model optimization and performance on embedded platforms.
  2. Design and implement robust MLOps pipelines to support continuous integration and deployment of AI models at the edge.
  3. Collaborate with cross-functional teams to integrate AI applications into embedded systems using GPUs and AI accelerators.
  4. Provide technical leadership and mentorship in embedded AI, system architecture, and AI deployment strategies.
  5. Drive innovation and problem-solving initiatives to enhance AI capabilities and deployment robustness on edge devices.

Skills

Required

  • embedded AI
  • GPU or AI accelerator deployment
  • edge model optimization
  • embedded system architecture
  • AI/machine learning algorithms
  • computer vision applications
  • MLOps pipelines for AI model deployment at the edge
  • problem-solving
  • innovation

Nice to have

  • system solutions with AI application deployment experience
  • embedded systems
  • real-time operating environments
  • collaborative, agile environments
  • Advanced degree in Computer Science, Electrical Engineering, or related technical field

What the JD emphasized

  • Proven experience in embedded AI with hands-on expertise in GPU or AI accelerator deployment.
  • Strong skills in edge model optimization and embedded system architecture.
  • Deep understanding of AI/machine learning algorithms and computer vision applications.
  • Experience building and managing MLOps pipelines for AI model deployment at the edge.

Other signals

  • Edge AI deployment
  • model optimization
  • MLOps pipeline development
  • embedded platforms
  • GPUs or AI accelerators