AI Product Manager

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,…