Associate Vice President - Product, AI & Innovation

THE ROLE

This role owns the full AI and product remit: our internal AI intelligence layer, the internal operating platform that runs the company, and the AI product engagements we deliver for enterprise clients. Not to maintain what exists, but to decide where it goes next.

This is a builders leadership role. You will spend part of your week in architecture discussions with engineers and part of it in front of client CXOs defending a roadmap. If you are looking for a pure strategy post with no proximity to the build, this is not that job.

WHAT YOU WILL OWN

• The AI intelligence layer. Our production internal AI platform, covering retrieval architecture, inference pipelines, a unified knowledge graph, and the roadmap for what it powers next across the organisation.
• The internal operating platform. An end to end system spanning project management, HRIS, CRM, client relations, workflow, finance and internal communication, replacing more than a dozen fragmented tools. You set sequencing, adoption strategy and the migration path off legacy SaaS.
• Client AI engagements. Scoping, technical proposals, commercial framing and delivery oversight for external AI builds. This includes writing and defending technical requirement documents, sizing infrastructure, and being the person in the room when a client asks why the architecture looks the way it does.
• AI governance. Ownership of the organisation wide AI usage and governance policy, covering access control models, data handling standards and responsible deployment review.
• The team. Hiring, structuring and developing the AI and product pod, including AI engineering and product ownership hires reporting into this role.

WHAT WE ARE LOOKING FOR

• Ten or more years across product, with at least three years shipping AI or ML products into production rather than into pilots.
• Demonstrable ownership of a platform, not only features. You have made architecture calls and lived with the consequences of them.
• Hands on literacy with LLM systems: retrieval architectures, agent design, evaluation methodology, and the practical failure modes of each. You do not need to write the production code, but you do need to recognise when a design will not hold.
• Experience scoping or selling technical work to enterprise buyers, including authoring the documents that carry the decision.
• Comfort operating across a wide band, from a sprint review to a board level narrative, in the same day.

USEFUL BUT NOT ESSENTIAL

• Cloud infrastructure depth, particularly GCP or AWS, including cost modelling for inference workloads.
• Published research, granted or filed patents, or other evidence of applied technical credibility.
• Experience in a services organisation where internal platform work and client delivery compete for the same engineering hours.

WHAT SUCCESS LOOKS LIKE

By month three you will have formed an independent view of both platform roadmaps and be able to argue for changing them. By month six you will be leading at least one client engagement end to end, with the pod structured around a delivery rhythm you designed.