What you'll be doing

Own the team and the outcome

• Lead a Compass or Tiger team — a small pod of engineers using agentic coding tools and a quality/evals specialist — from problem definition through to a live, working product.
• Set direction for the team day to day: what gets built, in what order, and why, grounded in client outcomes rather than output volume.
• Work hands-on alongside your engineers, using agentic coding tools yourself to prototype, unblock, and move fast.
• Assess the commercial value of what you're building before and after you build it — sizing the opportunity up front, then tracking adoption, revenue impact, and cost to serve once live — so the team's time goes on what actually moves the needle commercially, not just what's technically interesting.

Build AI-native products, not SaaS features with AI bolted on

• Design product experiences that anticipate and act on a client's behalf, rather than simply presenting information for a human to act on.
• Define and build evals so the team knows whether an AI feature is actually working — not just whether it shipped.
• Take AI products through to production, including the unglamorous parts: reliability, cost, latency, guardrails, and what happens when the model gets it wrong.

Discover and validate with real clients

• Run discovery directly with clients to understand where an intelligent, proactive product genuinely earns its place versus where it's AI for its own sake.
• Own the product analytics for your team's product — usage, adoption, retention, funnel, and cost/performance metrics — and use it alongside client feedback and evals results to decide what to build next.

Work across Product and Engineering

• Partner closely with the wider engineering teams on tooling, models, and platform capability.
• Bring back what you learn — on agentic ways of working, on evals, on what AI-native product development actually takes — to the wider Product team.

What we're looking for

• A product builder first. You can write a clear requirements doc when the situation calls for it, but you're just as comfortable in the codebase, prototyping alongside engineers using agentic coding tools (Claude Code or equivalent) as a regular part of how you work, not as an experiment.
• Real experience shipping AI products to production. Not a pilot that stayed a pilot — a live product, with real clients, that you built and are accountable for.
• Hands-on with evals. You know how to define what “good” means for an AI feature and how to measure it, and you've built or used evals frameworks to do so.
• Strong on product analytics. You're fluent in the numbers — usage, adoption, retention, funnel drop-off, cost, and performance metrics for AI features — and you use them, alongside evals results, to decide what the team builds next rather than relying on instinct or anecdote.
• Commercially switched on. You can build a business case, size an opportunity, and track it through to delivery — you treat commercial return as your responsibility, not something Finance checks after the fact.
• Comfortable with ambiguity. AI-native product development doesn't have a settled playbook yet. You're happy defining the approach as you go rather than waiting for one to be handed to you.
• Strong product fundamentals. Discovery, prioritisation, working with data and clients to decide what matters — the core PM craft still applies, it's just applied faster and closer to the build.
• B2B SaaS experience preferred, ideally in a multi-brand or multi-business-unit environment.

What's in it for you

• The chance to build genuinely AI-native products from the ground up, inside one of Citation's most consequential initiatives.
• A hands-on, builder-focused role at the leading edge of how AI is reshaping product development.
• Visibility across Product and Engineering leadership, with real ownership of what you ship.
• A supportive culture that prizes action, innovation, and getting great things done — for our clients and each other.