AI Product Manager

Role Overview

The AI Product Manager is a pivotal connector between business strategy and intelligent product delivery—translating complex organisational needs into clear, prioritised requirements and driving coordinated execution across Product Owners and cross-functional teams.

What You Will Do

The AI Product Manager shapes how AI and data-driven capabilities move from concept to production, leading requirements discovery, analysis, prioritization, and coordination with Product Owners and delivery teams.

Why It Might Be a Fit

This role requires strong analytical and communication skills, with the ability to translate business problems into clear data and modelling needs, and define success metrics that capture both model performance and business impact.

Requirements

• Leads requirements discovery across stakeholders through workshops, interviews, and process reviews.
• Elicits, documents, and validates business needs, user needs, pain points, and desired outcomes.
• Analyzes qualitative and quantitative inputs to refine requirements and recommendations.
• Supports the Product Leader with data-backed insights, business cases, and trade-off options.
• Partners with Product Owners to convert business requirements into well-groomed backlog items and sprint-ready work.
• Maintains continuous alignment between stakeholders and the delivery team.
• Defines and track requirement-level success measures.
• Gathers post-release feedback, triage issues/enhancements, and feed learnings back into the backlog.
• Leads feasibility framing for AI-enabled features: assess data availability, model complexity, and ROI before requirements are finalised.
• Translates business problems into clear data and modelling needs.
• Defines AI-specific success metrics alongside business metrics.
• Works closely with data scientists, ML engineers, and designers to align on experimentation approaches.
• Oversees post-launch monitoring requirements: define thresholds for model drift, bias, and performance decay.
• Applies AI ethics and governance principles and ensures privacy and compliance obligations are embedded into requirements.
• Communicates AI trade-offs clearly to non-technical stakeholders.
• Leads enterprise-scale GenAI roadmap planning, including prioritisation of knowledge management, conversational AI, document intelligence, and analytics use cases in alignment with organisational strategy and executive stakeholders.
• Embeds responsible AI lifecycle management into product requirements, including governance frameworks, bias and fairness reviews, and iterative oversight mechanisms throughout model deployment.
• Scopes and drives R&D initiatives for emerging AI patterns such as Retrieval-Augmented Generation (RAG) and autonomous AI Agents, translating innovation lab findings into scalable product capabilities.

Benefits

• Competitive salary
• Equity
• Healthcare
• PTO
• Retirement
• Learning budget
• Parental leave
• Wellness
• Visa/relocation
• Remote flexibility
• Stipends
• Bonus/commission
• Paid holidays