Senior Product Manager | Applied AI

Job description

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.

Responsibilities

- 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

Requirements

- 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

Nice to have

- 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