Head Artificial Intelligence and Digital R & D

Role mandate

Build applied AI and digital R&D capabilities that create defensible product intelligence for NIDOs automation platforms, moving ideas from experiment to validated pilot and then into production ownership.

What success looks like

• Create a focused AI/digital R&D roadmap tied to NIDO products and customer problems.
• Deliver validated POCs/pilots in vision, optimisation, predictive intelligence and related areas.
• Establish a repeatable experiment prove value pilot product decision productionise process.
• Transfer successful capabilities cleanly to Product Management and NIDO OS/Controls for industrialisation.
• Avoid AI for AIs sake; demonstrate measurable product/customer value.

Key responsibilities

• Identify high-value AI/ML, computer vision, optimisation, simulation/digital-twin and predictive use cases.
• Lead rapid experimentation, dataset strategy, model evaluation and technical feasibility work.
• Develop prototypes and pilot integrations with NIDO OS, Controls, sensors/cameras and test rigs.
• Define success metrics, validation plans, compute/data requirements and deployment constraints.
• Build partnerships with specialist vendors, universities or external experts where faster than internal build.
• Document and transfer validated technology into production teams with clear requirements and limitations.
• Maintain awareness of relevant technology shifts without chasing low-value trends.

Critical interfaces

Product Management & NPD • NIDO OS / Product Software • Controls Engineering • Systems Engineering • Reliability & Validation • Advanced Engineering & Physical R&D

Ideal candidate profile

Hands-on applied AI/ML or computer-vision leader who has built real-world systems, preferably where software interacts with physical equipment, cameras, sensors, industrial data or operations.

Experience & qualifications

• 1015+ years technology experience with 5+ years in applied AI/ML/vision or advanced digital R&D.
• Evidence of deployed or pilot-stage industrial/commercial AI, not only research or dashboards.
• Strong Python/ML ecosystem knowledge; architecture understanding; edge/real-time exposure preferred.
• Experience with computer vision, optimisation, anomaly detection, simulation or predictive systems relevant to automation is valuable.