Director, AI Engineering - Manufacturing
On-siteOttawa, Ontario, Canada or Pathum Thani, Pathum Thani, Thailand
Job Summary
Set and execute a multi-year roadmap for AI/ML applications in manufacturing, prioritizing opportunities in process optimization, yield improvement, visual inspection, predictive maintenance, and root-cause analysis. Guide the architecture, design, development, validation, and lifecycle management of production-grade systems while defining engineering patterns for data pipelines, feature management, and model performance. Partner with plant leadership and site teams to translate high-value problems into scalable solutions, ensuring adoption across diverse manufacturing environments. Establish governance for responsible, secure, and explainable AI, balancing local site responsiveness with reusable platforms. Lead a distributed team of AI/ML engineers across manufacturing sites, reporting to the Chief AI Officer. Requires 15+ years of experience in engineering management and AI/ML systems with deep practical knowledge of manufacturing operations. 30% travel required to partner locations.
Required Qualifications
- 15+ years of progressive experience in software engineering, AI/ML engineering, data science, or a closely related technical discipline, including significant leadership experience
- Bachelor's or master's degree in engineering, computer science, electrical engineering, industrial engineering, data science, or a related field
- Deep hands-on understanding of software engineering practices, distributed systems, cloud or edge architectures, APIs, data platforms, testing, observability, security, and production operations
- Practical knowledge of manufacturing processes, process variation, yield, quality systems, equipment data, inspection, traceability, root-cause analysis, and the realities of plant operations
- Willingness and ability to travel 30% or more to manufacturing sites and partner locations
Desired Qualifications
- advanced technical education
- Experience in semiconductor, photonics, electronics, optical components, precision manufacturing, or other high-volume advanced manufacturing environments
- Experience with manufacturing software and data ecosystems such as MES, SPC, QMS, ERP, equipment automation, inspection platforms, historian systems, and yield management systems
- Experience scaling AI/ML capabilities across multiple plants, regions, or business units with different processes, data maturity, and operating models
- Experience applying generative AI, knowledge retrieval, agentic workflows, or LLM-enabled tools to manufacturing, engineering, quality, or operations use cases
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