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.