AI Accelerator Lead

In this role you lead the AI Accelerator at Ordnance Survey, steering rapid prototyping and capability building to unlock enterprise-wide value from AI. You will bridge tech leadership with programme delivery, coordinating across product, data and tech teams to embed responsible AI, with a focus on geo AI techniques and scalable, production-ready prototypes. You influence strategy, governance, and learning whilst delivering high-impact results that reduce adoption friction. This is a chance to shape AI-enabled geospatial solutions at scale in a collaborative, innovative environment.

• salary up to 92,258 GBP
• performance related bonus up to 10%
• pension scheme (OS contributes up to 12.07%)
• 28 days annual leave + bank holidays
• hybrid working
• OS Maps subscription

• Design and operate the AI Accelerator as a high-impact programme for experimentation and capability development across OS
• Explore emerging AI techniques, identify high-value applications, and assess risks and opportunities for the business
• Own prototype design and production-grade implementation, oversee code quality and transition to scalable solutions
• mentor participants, energise the AI Guild, and curate learning resources to foster responsible experimentation
• Collaborate with product, data, and technology teams to align initiatives with strategic priorities and ethics
• Track, communicate outcomes and recommendations to senior stakeholders to inform roadmaps and investment

• Proven experience in AI/ML engineering, software development, and technical leadership
• Strong understanding of LLMs, agentic systems, and orchestration frameworks
• Ability to write production-grade code and define engineering best practices
• Experience in programme design, stakeholder engagement, and impact evaluation
• Passion for responsible AI adoption, including human-in-the-loop workflows and ethical application
• Familiarity with AI governance, regulatory compliance, and secure software engineering
• Ability to mentor, inspire, and influence across diverse teams
• Commitment to continuous learning and staying current with AI technologies

• stakeholder engagement
• leadership
• communication
• AI/ML engineering
• LLMs
• agentic systems