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.#1100334046

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 team's 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 team's 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