Director Embedded AI Engineering

Honeywell; Atlanta (GA); On-site; USD 120,000 - 150,000; Full time

Job summary

Honeywell is looking for a hands-on lead in Atlanta, Georgia, specializing in Edge AI deployment. The role focuses on optimizing and deploying AI models at the edge, requiring deep expertise in embedded AI and a strong background in GPUs and MLOps. Ideal candidates will have a passion for innovation, along with strong problem-solving skills and experience in AI application deployment within embedded systems. This position provides opportunities to drive cutting-edge AI solutions and work collaboratively in dynamic environments.

Qualifications

• 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.
• Excellent problem-solving skills and passion for innovation in embedded AI technologies.

Responsibilities

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

Skills

Embedded AI experience

GPU optimization

MLOps pipeline management

System architecture knowledge

Problem solving

Computer vision applications

Education

Advanced degree in Computer Science or Electrical Engineering

Job description

Job Summary

This role is for a hands‑on lead specializing in Edge AI deployment. The successful candidate will provide specialized expertise in model optimization at the edge, robust deployment, and MLOps pipeline development. You will leverage your skills in edge optimization, system and embedded knowledge, AI/machine learning, MLOps, computer vision, innovation, and problem solving to drive advanced AI solutions. We are seeking someone with embedded AI experience, particularly with GPUs or AI accelerators, and strong system solution knowledge for AI application deployment. Based in Atlanta, Georgia.

Responsibilities

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

Qualifications

• 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.
• Excellent problem‑solving skills and passion for innovation in embedded AI technologies.

We Value

• Background in system solutions with AI application deployment experience.
• Strong knowledge of embedded systems and real‑time operating environments.
• Experience working in collaborative, agile environments.
• Advanced degree in Computer Science, Electrical Engineering, or related technical field preferred.

U.S. Person Requirements

• Due to compliance with U.S. export control laws and regulations, candidate must be a U.S. Person which is defined as a U.S. citizen, a U.S. permanent resident, or have protected status in the U.S. under asylum or refugee status or have the ability to obtain an export authorization.