Director of Applied AI

Overview

As Director of Applied AI, you will turn data into measurable business value by leading AI across design, manufacturing, tooling, and QC. You will shape the AI strategy and end-to-end roadmap, partnering with software, IT, and operations to deploy real systems that improve quality, efficiency and throughput. You will build and lead an AI Engineering function, delivering MVPs and scale-ready solutions in a fast-moving, industrial setting. This role offers the chance to impact global manufacturing at iCOMAT and contribute to its mission of redefining lightweight structures.

Pay / Benefits

• private healthcare
• pension scheme
• stock options
• career growth opportunities
• innovation-focused culture
• global impact

Responsibilities

• Advance predictive quality through AI-assisted process monitoring and root-cause analysis
• Automate design decisions and enable generative design to shorten iteration cycles
• Accelerate tooling design and optimize tool life with intelligent path generation
• Improve manufacturing throughput, reduce scrap, and predict defects before they occur
• Own the AI strategy and roadmap, aligned with the CEO and senior leadership
• Manage a portfolio of MVPs across design, manufacturing, tooling, and QC
• Set data architecture choices enabling AI across the product lifecycle in partnership with software and IT
• Oversee tooling and model selection, balancing build/buy/fine-tune options
• Develop a KPI dashboard to track AI-driven business gains in real time
• Scale the AI function and report progress to the CEO

Key requirements

• Shipped applied machine learning solutions in industrial, manufacturing, robotics or hard-tech settings
• MLOps and production deployment at scale
• Strong ability to defend AI investments with metrics and business impact
• Senior individual contributor who can lead by example and collaborate cross-functionally
• Comfort with ambiguity, fast cadence, and ownership
• Experience working with software, IT, design, manufacturing, tooling, and QC leaders

• collaboration without ego
• ownership and accountability
• execution focus
• MLOps and production deployment at scale
• AI engineering in real industrial settings
• computer vision for automated inspection

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