DIRECTOR, AI-DRIVEN MANUFACTURING TRANSFORMATION | SINGAPORE

DIRECTOR, AI-DRIVEN MANUFACTURING TRANSFORMATION | SINGAPORE

About the opportunity

A large-scale manufacturer operating at the leading edge of high-tolerance equipment and advanced hardware is building out a new layer of leadership around artificial intelligence — someone who can sit at the intersection of deep tech and factory-floor reality, and make AI a genuine driver of how the business builds things. This isn't a research role bolted onto operations; it's a mandate to define, build, and scale AI capability as a core part of how the company competes.

What success looks like in this role

Within the first year or two, you'll have:

• Established a clear point of view on where AI creates the most value across the manufacturing base, and gotten leadership aligned behind that direction
• Delivered at least one or two tangible technical wins — likely in areas like equipment reliability forecasting, process yield improvement, or next-generation automation — that prove the model can move from lab to line
• Helped standard product lines become measurably smarter and more connected, with adoption happening in practice, not just on paper
• Worked with engineering stakeholders to get common technical standards defined and actually followed across multiple equipment types
• Stood up the foundational data, modeling, and software capability the rest of the organization can build on going forward

What we're looking for

Candidates should bring a decade or more of hands-on experience in industrial AI or advanced manufacturing, ideally including several years where you personally owned an AI or technology function rather than reporting into one. Academically, a graduate degree in a technical field (AI, computer science, engineering, applied math, or similar) is expected; a doctorate is a strong plus but not a hard requirement.

The stronger candidates in this search tend to have:

• A portfolio of AI work that made it into production, not just prototypes — data platforms that got used, models that shipped, projects that ran the full distance from idea to deployment
• Fluency across the core AI toolkit (machine learning, deep learning, reinforcement learning) and enough exposure to emerging areas — think next-gen robotics or large language models — to have an informed opinion on where they fit industrially
• Hands-on fluency architecting industrial software systems — PHM, MES, SCADA, APS, and similar — not just AI models built in isolation from how a plant actually runs
• The judgment to translate messy, ambiguous factory-floor problems into workable technical solutions, and the communication skills to bring skeptical stakeholders along

This is a highly cross-functional role inside a large, matrixed organization, so the ability to build trust and alignment without formal authority matters as much as technical depth.