Principal Product Mgr I

Principal Product Manager – LeapSpace, UK

Lead the engine that powers LeapSpace’s AI‑assisted research workspace. Own the product strategy, vision, and roadmap for agentic retrieval and orchestration, and drive its evolution from prototype to production.

Responsibilities

• Own the product strategy, vision, and roadmap for agentic retrieval and orchestration.
• Make sequencing decisions that span multiple teams, determining what ships first, what waits on dependencies, and the cost of each answer.
• Lead the staged migration of new engine capabilities from prototype to production, setting incremental releases and refining targets.
• Translate validated prototype behavior into engineering‑ready work—tickets with clear acceptance criteria and a technical approach refined with the team.
• Keep squad teams (UX, design, data science, and others) informed ahead of engine changes, briefing each group early to enable downstream planning.
• Support other squads’ agentic requirements by incorporating their requests into the engine backlog and consolidating shared needs into platform capabilities.
• Anticipate demand on the agentic system from the product roadmap—new sources, formats, and workflows—and position the engine accordingly.
• Expand LeapSpace’s surface area by growing tools and skills, consolidating user tasks and personas, and collaborating with partner teams across Elsevier.
• Collaborate with external partners across Elsevier to develop capabilities that span products.

Requirements

• 10+ years building and shipping complex AI or data‑intensive products, with sustained ownership of strategy, trade‑offs, discovery, and delivery.
• At least three consecutive years at one employer, with experience more important than a specific title.
• Deep expertise in generative AI products, including retrieval‑augmented generation, agentic workflows, and their evaluation.
• Proven ability to independently drive product strategy and execution across multiple engineering organisations and external dependencies.
• Strong go‑to‑market experience and skill in hypothesis‑driven product evaluation, including A/B testing and experimentation frameworks.
• Comfortable with AI coding tools and rapid prototyping; able to build proofs of concept and validate ideas alongside engineering and data science teams.
• Experience in customer discovery within academic, research, or professional information markets is highly desirable.
• Technically fluent with sound architectural judgment, able to reason about system boundaries, trade‑offs, and engage with architects, AI researchers, and platform teams.

If you are interested, please apply with a cover letter and CV.

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