Director, AI Engineering - Manufacturing

It's fun to work in a company where people truly BELIEVE in what they're doing!
We're committed to bringing passion and customer focus to the business.

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

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.