VP, AI

Key Responsibilities

• Modernize Engineering Workflows: Lead the rollout of AI coding assistants, automated testing, and development tools to improve day-to-day engineering speed and quality.
• Upskill Teams: Create practical guidelines, training, and playbooks to help our engineering teams adopt AI-assisted development practices.
• Build Shared Infrastructure: Develop reusable, centralised AI components (like API gateways, retrieval architectures, and evaluation tools) so teams don't have to build them from scratch.
• Simplify Product Prototyping: Help product managers and designers use AI tools to quickly mock up, test, and validate ideas.
• Integrate AI Features: Partner with product leaders to build reliable AI capabilities directly into our core products (such as royalty processing, publishing, and distribution).
• Workplace Automation: Collaborate with operational teams to identify and automate repetitive, data-heavy manual processes to improve speed and reduce errors.
• Utilise Our Data Asset: Work with Data Engineering and Data Science teams to build and deploy proprietary AI/ML models for use within our products. Focus on implementation of models and approaches that deliver value to our customers.
• Governance & Standards: Set clear standards for model quality, data privacy, and intellectual property compliance in partnership with our Security, Legal, and IT teams.

Candidate Profile

• Hands-on AI/ML Leadership: Experience building and deploying practical ML models in a production environment (not just calling third-party APIs).
• Improving Engineering Workflows: Experience rolling out modern developer tooling (such as Copilot or similar assistants) across engineering teams.
• Collaborative Change Management: A track record of helping teams adopt new technologies and processes through influence and support rather than top-down mandates.
• Pragmatic Mindset: Highly focused on shipping functional software and solving real problems, with a realistic view of AI's current limitations.