Senior Product Manager | Applied AI

We are growing a product-management team that runs AI-powered products and applies AI across the whole product lifecycle. As a Senior Product Manager Applied AI you will own the vision strategy roadmap and delivery of one or more products at least one of them AI-powered while using AI day-to-day to work faster and make sharper decisions and mentoring other Product Managers as you do.

What Applied AI means here. This is a product role not an engineering role. We expect confident practical command of AI as a product manager using it across the lifecycle and shaping AI-powered features not the ability to build train or tune machine-learning models as an ML engineer or data scientist would.

Req#

• Own the vision strategy roadmap and end-to-end delivery of one or more products including at least one that is AI-powered
• Define requirements and use cases for AI features treating them as probabilistic products scoping data and use-case fit and setting evaluation criteria quality bars guardrails and human-in-the-loop review as acceptance criteria
• Identify where AI can improve the product the customer experience internal processes or business outcomes and build and size the case for it
• Use AI tools productively and with discipline across discovery research synthesis analysis documentation user stories prototyping and planning verifying outputs before relying on them
• Prioritise features own the backlog and manage release cycles using AI-assisted analysis to support (not replace) your decisions
• Manage the full product lifecycle including the added considerations of AI features model/version changes quality drift and human oversight
• Work closely with engineering data design and AI/ML teams translating product intent into requirements they can act on and their constraints into product decisions
• Set realistic expectations with clients and stakeholders about what AI can and cannot do and communicate benefits limitations and risks in plain language
• Mentor Product Managers and raise the teams practical AI capability by example

• 5 years in Product Management having managed one or more products end-to-end including post-launch maintenance and support
• Launched more than one product (or key capability) to market with at least one experience delivering or materially improving an AI-powered product or feature (e.g. GenAI ML recommendations search or automation)
• Solid ownership of product strategy vision and roadmap; market analysis and product visioning including identifying and justifying where AI adds value
• Experience owning backlogs cross-product dependencies and release cycles and managing product lifecycle and support models
• Familiarity with product profitability competitive positioning and pricing including the cost latency and quality trade-offs that shape AI-feature economics
• Expertise across multiple (3) business domains able to act as a business-domain SME
• Working AI literacy what current AI (including GenAI/LLMs) can and cannot do reliably common patterns and typical failure modes enough to make sound product decisions and hold credible conversations with technical teams. Deep model-building knowledge is not required
• Able to influence stakeholders up to and including VP level and to lead all critical aspects of a product launch
• Able to lead a team of Product Owners and/or Product Managers across a product line or family guiding them through the full lifecycle
• Raises the teams practical AI proficiency sharing verified ways of working and coaching on requirements and evaluation criteria for AI features
• Practical daily use of AI across research discovery analysis documentation user stories prototyping and planning with verification of outputs
• Ability to identify and justify AI opportunities in products processes CX and business outcomes
• Working AI literacy concepts terminology capabilities and limitations at PM depth
• Experience defining requirements and use cases for AI features including evaluation criteria quality bars guardrails and human oversight
• Ability to evaluate AI outputs for quality accuracy risk limitations and user impact
• Responsible-AI awareness privacy bias security transparency and human oversight
• Ability to work effectively with engineering data design and AI/ML teams
• Adaptability and continuous learning as AI tools and capabilities evolve

• Hands-on prototyping with AI builder tools to near-production fidelity
• Building PM agents / multi-step automations across research backlog analytics and comms
• Understanding of AI-feature economics (cost latency unit economics) and model lifecycle (drift versioning vendor change)
• Familiarity with AI regulation (e.g. EU AI Act) and relevant sector rules
• Prior experience taking an AI-powered product to production at scale