AI Product Manager: LLM & Agentic Workflows in London

Role Responsibilities & Key Accountabilities

• Contribute to the full AI product lifecycle: discovery, requirements definition, development, testing, and deployment.
• Design, build, and iterate on LLM-powered agentic workflows for complex, data-intensive use cases, applying sound orchestration patterns and tool-use design.
• Translate business and user needs into clear, actionable product requirements and agent configurations.
• Define and monitor product performance metrics and acceptance criteria for AI outputs in production — covering accuracy, latency, cost, and auditability.
• Manage the post-launch product lifecycle: track performance, gather user feedback, and contribute to model or feature refresh cycles.
• Contribute to system optimisation across performance, cost, and operational constraints.
• Collaborate with governance teams to ensure AI outputs meet internal quality, compliance, and interoperability standards.
• Maintain a forward-looking view on the evolving AI landscape — including model capabilities, agentic frameworks, and emerging protocol standards — and translate relevant developments into product opportunities.
• Engage with internal stakeholders and cross-functional teams to support successful delivery of AI capabilities.
• Support demos and presentations of prototypes and new capabilities.
• Build and share expertise in AI product design and agentic workflows across engineering, product, and domain teams.

Qualifications & Experience

• 7+ years of experience spanning software or ML engineering and product development, or a closely related combination.
• Demonstrated hands‐on experience building with LLMs and/or agentic frameworks — shipped products or features preferred.
• Working knowledge of how large language models and agentic systems behave in production — including tool use, prompt design, orchestration patterns, output variability, and failure modes.
• Ability to write clear product requirements and define, review, and challenge technical specifications without requiring engineering support.
• Experience evaluating and testing AI outputs — defining acceptance criteria, identifying edge cases, and working with engineering teams to resolve model or integration issues.
• Solid Python skills and familiarity with APIs, data pipelines, and cloud infrastructure.
• Experience with real-time or near-real-time data systems, with a natural sensitivity to latency, throughput, and cost trade-offs.
• Familiarity with responsible AI principles — including data quality, model performance monitoring, and bias considerations — and their implications for product design in regulated environments.
• Comfortable working across technical and commercial stakeholders — able to translate product decisions clearly for engineering teams and client‐facing audiences alike.
• Exposure to AI partner platforms or ecosystems in a product, technical, or commercial capacity is an advantage.
• BA, BS, or Master's degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent practical experience.

Career Stage: Manager

Benefits: Health care, retirement planning, paid volunteering days, wellbeing initiatives, and tailored benefits and support.

Equal Opportunity Statement: We are proud to be an equal opportunities employer. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, gender identity, gender expression, age, marital status, veteran status, pregnancy or disability, or any other basis protected under applicable law.