Director, AI Engineering - Manufacturing

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Role Summary

We are seeking a senior engineering and AI/ML leader to build and lead a distributed team of AI/ML engineers supporting manufacturing sites across the company. Based in Bangkok or the UK or Canada and reporting to the Chief AI Officer, this role will translate high-value manufacturing problems into reliable, scalable AI systems that improve productivity, quality, yield, equipment performance, and operational decision-making.

The ideal candidate brings 15+ years of experience across engineering management, software engineering, AI/ML systems, and data science, combined with deep practical knowledge of manufacturing environments. This leader must be equally comfortable setting technical direction, coaching engineers, partnering with plant and business leaders, and working close to the factory floor to move solutions from discovery through production adoption.

Leadership Scope

• Lead, grow, and develop a distributed team of AI/ML engineers who work across manufacturing sites and solve site-specific operational problems.
• Establish the technical vision, engineering standards, delivery practices, and talent strategy for manufacturing AI across the organization.
• Create a strong partnership model with plant leadership, manufacturing engineering, process engineering, yield, quality, operations, IT, and enterprise data teams.
• Balance local site responsiveness with reusable platforms, common architectures, shared components, and responsible AI practices that scale across plants.

Key Responsibilities

• Set and execute a multi-year roadmap for AI/ML applications in manufacturing, aligned to business priorities and measurable operational outcomes.
• Identify and prioritize high-impact opportunities across process optimization, yield improvement, visual inspection, defect classification, predictive maintenance, anomaly detection, root-cause analysis, quality, scheduling, and engineering productivity.
• Guide the architecture, design, development, validation, deployment, and lifecycle management of production-grade AI/ML systems used in manufacturing workflows.
• Define engineering patterns for data pipelines, feature and model management, experiment tracking, APIs, user interfaces, monitoring, cybersecurity, reliability, and model performance management.
• Ensure solutions work with real manufacturing data and systems, including MES, SPC, QMS, equipment and sensor data, inspection systems, yield systems, ERP, databases, engineering reports, and other structured and unstructured sources.
• Drive disciplined problem definition and value measurement, connecting technical delivery to improvements in cycle time, throughput, yield, scrap, quality, cost, safety, and decision speed.
• Partner with site teams to understand workflows, constraints, process variation, data quality, user needs, and adoption barriers; ensure solutions are usable by engineers, operators, and business stakeholders.
• Build and maintain strong relationships with senior manufacturing and technology leaders, communicating technical tradeoffs, risks, investment needs, and results with clarity.
• Promote effective use of classical machine learning, deep learning, computer vision, optimization, statistical methods, LLMs, and generative AI where they are fit for purpose.
• Establish governance for responsible, secure, explainable, and maintainable AI in manufacturing, including validation, human oversight, change control, and production support.

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; advanced technical education is preferred.
• 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.

Preferred Qualifications

• 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.