AI Product Manager
PathwaysX, Inc.
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Benefits offered by this job
Job summary
PathwaysX in the United Kingdom is seeking an AI Product Manager to define product strategy and lead the development of AI-powered products from discovery through launch.
You will translate customer needs into requirements, prioritise the roadmap, collaborate with engineering and data science teams, and measure success with defined metrics, balancing business value with model capabilities.
Qualifications
- Sets a clear product vision and strategy aligned to business goals.
- Builds and maintains a prioritised roadmap with measurable objectives.
- Leads products through the full lifecycle from discovery to launch.
- Gathers customer requirements and identifies product opportunities.
- Converts business needs into user stories and acceptance criteria.
- Prioritises features using value, effort, risk, and customer impact criteria.
- Demonstrates working knowledge of AI/ML capabilities, constraints, and failure modes.
- Evaluates AI output quality, reliability, and user experience.
Responsibilities
- Define product vision, strategy, roadmap, and measurable objectives.
- Gather customer requirements and identify product opportunities.
- Convert business needs into user stories and acceptance criteria.
- Prioritise features using value, effort, risk, and customer impact criteria.
- Collaborate with AI engineers, ML engineers, designers, and data scientists.
- Define success metrics and monitor product performance.
- Coordinate product testing, user acceptance testing, and launches.
- Evaluate AI output quality, reliability, and user experience.
- Maintain product documentation and communicate roadmap changes.
- Collect feedback and continuously improve released features.
Skills
Job description
The AI Product Manager will define product strategy and lead the development of AI-powered products from discovery through launch. The role involves translating customer and business needs into product requirements, prioritising the product roadmap, collaborating with engineering and data science teams, and evaluating AI product performance.The successful candidate should understand AI/ML capabilities and limitations, communicate effectively with technical and non-technical stakeholders, and deliver measurable customer and business value.
Health Insurance
Private healthcare coverage supporting physical and mental wellbeing.
Flexible Work
Hybrid working arrangements with flexibility around work location and schedules.
Paid Time Off
Paid annual leave to support rest and work-life balance.
Learning & Budget
Dedicated budget for AI/ML courses, technical certifications, conferences, and professional development.
Retirement Plan
Wellness / Stipend
Wellbeing allowance for approved health, fitness, and wellness activities.
- 01 Define product vision, strategy, roadmap, and measurable objectives.
- 02 Gather customer requirements and identify product opportunities.
- 03 Convert business needs into user stories and acceptance criteria.
- 04 Prioritise features using business value, effort, risk, and customer impact.
- 05 Collaborate with AI engineers, ML engineers, designers, and data scientists.
- 06 Define success metrics and monitor product performance.
- 07 Coordinate product testing, user acceptance testing, and product launches.
- 08 Evaluate AI output quality, reliability, and user experience.
- 09 Maintain product documentation and communicate roadmap changes.
- 10 Collect feedback and continuously improve released features.
Must have
- Sets a clear product vision and strategy aligned to business goals
- Builds and maintains a prioritised roadmap with measurable objectives
- Leads products through the full lifecycle from discovery to launch
Nice to have
- Experience owning a product roadmap end-to-end
- Track record of shipping products through defined discovery-to-launch processes
Must have
- Gathers customer requirements and identifies product opportunities
- Converts business needs into user stories and acceptance criteria
- Prioritises features using value, effort, risk, and customer impact criteria
Nice to have
- Experience writing user stories and acceptance criteria
- Use of structured prioritisation frameworks in past roles
Must have
- Demonstrates working knowledge of AI/ML capabilities, constraints, and failure modes
- Evaluates AI output quality, reliability, and user experience
- Makes product trade-offs informed by model behaviour and data considerations
Nice to have
- Prior experience managing or working closely on ML/AI-powered features
- Ability to discuss model limitations credibly with engineering and data science teams
Must have
- Collaborates effectively with engineering, data science, and design teams
- Communicates roadmap changes and product decisions to diverse stakeholders
Nice to have
- Experience bridging technical and business audiences
- History of coordinating multi-disciplinary product teams
Must have
- Defines success metrics and monitors product performance against them
- Coordinates product testing, user acceptance testing, and launches
- Collects feedback and iterates on released features to deliver measurable value
Nice to have
- Experience running UAT or structured launch processes
- Evidence of using data/feedback loops to improve a shipped product
Cross-functional collaboration with data scientists, AI engineers, software developers, and product teams.
Weekly sprint planning, model performance reviews, technical discussions, and progress updates.
Data-driven collaboration focused on experimentation, knowledge sharing, and joint problem-solving.
Clear, evidence-based communication of model performance, technical risks, and recommendations.
Decisions supported by model evaluation results, experimental evidence, data quality, and business requirements.
Documented experiments, model versions, evaluation metrics, technical decisions, and implementation processes.
Employees take responsibility for AI initiatives, technical decisions, and measurable business outcomes.
Teams share constructive feedback through model reviews, code reviews, and product retrospectives.
AI decisions, model limitations, experiment results, and project risks are communicated openly.
Teams explore emerging AI/ML technologies to build useful, scalable, and differentiated products.
Employees continuously develop skills in machine learning, data science, model evaluation, and responsible AI.
Data scientists, ML engineers, software engineers, and product teams work together toward shared goals.
The AI Product Manager has reviewed existing product strategy, roadmap, and backlog, met with engineering, data science, and design stakeholders, and identified early opportunities and risks in current AI product performance.
The AI Product Manager has established or refined a prioritised roadmap with clear success metrics, translated key customer and business needs into actionable user stories, and coordinated at least one round of testing or a feature launch with cross-functional teams.
The AI Product Manager consistently delivers AI-powered features that meet defined success metrics, maintains a roadmap that balances customer impact with technical feasibility, and drives continuous improvement through feedback-informed iteration and reliable cross-team collaboration.
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