We are looking for an experienced Senior AI Product Owner with a proven track record of successfully managing and delivering AI initiatives from idea to production.
This position requires much more than general Product Owner experience or an interest in Artificial Intelligence. The successful candidate must have real, hands-on experience leading AI-driven products or initiatives, working closely with business stakeholders, Data Scientists, AI/ML Engineers, architects and delivery teams.
The AI Product Owner will take ownership of the product vision, roadmap, backlog and delivery priorities, while ensuring that AI initiatives address concrete business needs and generate measurable business value.
Key Responsibilities
Own and drive AI product initiatives from ideation and use-case definition through delivery, deployment and continuous improvement.
Define the product vision, objectives, roadmap and success criteria for AI-enabled products and solutions.
Identify, assess and prioritise AI use cases based on business value, feasibility, risk and implementation effort.
Translate business challenges into actionable AI product requirements.
Build, maintain and prioritise the Product Backlog.
Write and refine epics, features, user stories and acceptance criteria.
Work closely with Data Scientists, AI/ML Engineers, Software Engineers, Architects and business teams.
Facilitate alignment between business expectations and technical/AI capabilities.
Lead backlog refinement and actively participate in Scrum ceremonies, including Sprint Planning, Reviews and Retrospectives.
Manage dependencies, priorities, trade-offs and stakeholder expectations.
Define and monitor relevant KPIs and success metrics for AI initiatives.
Ensure that AI solutions move beyond experimentation and Proofs of Concept toward usable, scalable and production-ready solutions.
Contribute to decisions regarding AI governance, data requirements, model performance, explainability, security and responsible AI.
Continuously assess product performance, user feedback and business impact to define subsequent iterations.
Required Experience & Skills
We are specifically looking for candidates with demonstrable, hands-on experience delivering AI initiatives.
The candidate must have:
Several years of professional experience as a Product Owner / Senior Product Owner / AI Product Owner.
Proven experience managing AI, Machine Learning, Generative AI or data-driven product initiatives.
Demonstrable experience taking at least one significant AI initiative through multiple stages of its lifecycle, ideally from business use case through implementation and production.
Strong understanding of the specific challenges associated with AI products, including data quality, model performance, experimentation, uncertainty and continuous improvement.
Experience working directly with Data Science, AI/ML and Engineering teams.
Strong Product Management and Product Ownership capabilities.
Excellent command of Agile and Scrum methodologies.
Scrum certification is mandatory (PSPO, CSPO or equivalent recognised certification).
Strong experience in backlog management, user story definition, prioritisation and acceptance criteria.
Ability to assess AI opportunities from both business-value and feasibility perspectives.
Strong stakeholder management and communication skills.
Ability to challenge both business expectations and technical proposals constructively.
Strong analytical, decision-making and problem-solving capabilities.
Ability to work autonomously and take full ownership of the product.
Important: General Product Owner experience combined with theoretical AI knowledge is not sufficient for this role. Candidates must be able to demonstrate real AI initiatives they have personally managed and delivered, their specific responsibilities, the challenges encountered and the business outcomes achieved.
AI Experience We Expect
Candidates should be comfortable discussing concrete AI projects they have delivered, including topics such as:
The business problem and AI use case addressed.
Their personal role and ownership in the initiative.
How the use case was prioritised and validated.
Data availability and data-quality considerations.
Collaboration with Data Science / AI / Engineering teams.
PoC or MVP definition and validation.
Model or solution evaluation criteria.
Transition from experimentation to production.
User adoption and change management.
Business KPIs and measurable outcomes.
AI-related risks, governance and responsible AI considerations.
Experience with Generative AI, LLM-based solutions, intelligent automation, predictive models, recommendation systems or other enterprise AI applications would be particularly valuable.
Languages
English: Fluent – mandatory
French: An asset
Contract & Availability
Both Freelance and Permanent (CDI) profiles are welcome.
Immediate availability is strongly preferred.