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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Product Owner (for AI Delivery) - **Company:** Euroclear - **Location:** Belgium - **Contract:** Permanent contract - **Skills:** Agile Methodology, Artificial Intelligence, Data Transformation, Machine Learning, Data Logging, Feature Engineering, Data Pipelines - **Published:** June 10, 2026 - **Apply:** https://be.indeed.com/viewjob?jk=22070345627bff7d ## About the Role Do you have experience in Machine learning? ## Description The Product Owner is the guardian of product fitness for purpose , ensuring that functional, non-functional, and AI-specific requirements are met for products of limited complexity, uncertainty, and dependencies (e.g. mature products, end-of-life systems, or products with a well-defined operational scope). This includes ensuring that AI components (models, data pipelines, decision logic, automation) are: * Fit for business intent * Compliant with regulatory and ethical standards * Operationally robust and explainable Description & Responsibilities 1. Product Ownership & Business Value (AI-Aware) * Act as end-to-end owner of the product , including: + Functional requirements + Non-functional requirements (performance, security, resilience) + AI-specific qualities such as explainability, data quality, bias awareness, and model lifecycle sustainability * Link business value to the Product Backlog , explicitly identifying: + Where AI or automation contributes to efficiency, risk reduction, or customer value + Where non-AI solutions are preferable , ensuring pragmatic and value-driven decisions * Represent the business intent behind AI usage , ensuring the squad understands: + Why AI is used + What decisions it supports or automates + What human oversight is required 2. Stakeholder & Customer Centricity (AI Context) * Identify and manage stakeholders (business sponsors, operations, risk (EU AI Risks associated as well), compliance, legal, IT, data, architecture). * Collect and federate stakeholder input on: + Business outcomes + Regulatory constraints + AI acceptability (risk appetite, explainability, auditability) * Guide the squad towards customer-centric and user-centric AI solutions , ensuring: + Transparency of AI-driven decisions + Clear communication of AI limitations and confidence levels 3. Backlog Management & Story Definition (AI-Ready) * Own and manage the Product Backlog , ensuring it is: + Complete, transparent, prioritized, and understood + Inclusive of AI lifecycle work , not just features * Effectively write and slice stories that may include: + Data sourcing and preparation + Feature engineering (transforming raw data into model-ready inputs) + Model inference integration (how predictions are consumed by systems) + Human-in-the-loop controls (human validation or override of AI outputs) * Ensure stories include AI-relevant acceptance criteria , such as: + Accuracy or quality thresholds + Explainability requirements + Monitoring and logging expectations 4. Collaboration with Epic Owner & TPO (AI Alignment) (TPO = Technical Product Owner, responsible for technical coherence) * Work closely with the Epic Owner and TPO to: + Maximize business value from AI and data capabilities + Align AI initiatives with strategic priorities at epic and feature level + Co-own business objectives, including AI-enabled outcomes * Refine Features into Product Backlog Items (PBIs) that reflect: + Business intent + Technical feasibility + AI risk and compliance constraints 5. Delivery Oversight & Risk Management (AI & Regulatory) * Oversee delivery stages and ensure all risks are identified and mitigated , including: + Regulatory risks (e.g. CSDR - Central Securities Depositories Regulation) + Compliance and data protection (e.g. GDPR - General Data Protection Regulation) + Security and architecture risks + AI-specific risks : o Model bias o Lack of explainability o Data drift (changes in data patterns over time) o Model drift (degradation of model performance in production) * Ensure AI solutions comply with: + Internal AI governance frameworks + Model risk management expectations + Audit and traceability requirements 6. Sprint Execution & Value Validation * Define with the squad: + Sprint goals + Sprint content + Readiness of AI-related work (data availability, environments, dependencies) * Facilitate sprint reviews and demonstrations, ensuring: + AI outcomes are explained in business terms + Limitations and confidence levels are transparently communicated * Validate and accept or reject delivered stories and features, including: + Verification that AI outputs meet agreed acceptance criteria + Confirmation that monitoring and controls are in place 7. Measurement, KPIs & Continuous Improvement (AI-Informed) * Define and pilot Product and Business KPIs , with support from senior colleagues, including: + Traditional KPIs (throughput, adoption, value delivered) + AI-specific indicators , such as: o Prediction quality trends o Automation rates vs. manual intervention o Exception and override frequency * Actively collect feedback from the squad and stakeholders and translate it into backlog improvements. * Assess and demonstrate value delivered at squad level (e.g. squad health check boards), ensuring AI contributions are measurable and defensible . Role Scope & Support * Operates on products of limited complexity, uncertainty, and dependencies , such as: + Mature or end-of-life products + Well-defined operational scopes + AI components with controlled impact and clear governance * Receives guidance from senior colleagues for: + Strategic decisions + Complex prioritization trade-offs + AI-related risk or compliance decisions Key Competencies (AI-Infused) * Strong Product Ownership fundamentals (Agile, backlog management, value prioritization) * AI and data literacy , including: Understanding of the AI lifecycle (data model deployment + monitoring) + Ability to translate business needs into AI-ready requirements * Awareness of AI governance, compliance, and ethical considerations * Ability to collaborate effectively with: + Data Scientists + Machine Learning Engineers + Architects and Risk/Compliance stakeholders Final Note (Positioning) This role does not require hands-on model building , but it does require sufficient AI technology stack understanding to: * Ask the right questions * Prioritize the right work * Ensure AI delivers real, compliant, and sustainable business value ## Related Videos - [Introduction to Azure Machine Learning](https://www.wearedevelopers.com/videos/368-introduction-to-azure-machine-learning) - [Unleashing the power of AI to prevent financial crime](https://www.wearedevelopers.com/videos/1088-unleashing-the-power-of-ai-to-prevent-financial-crime) - [Why and when should we consider Stream Processing frameworks in our solutions](https://www.wearedevelopers.com/videos/1085-why-and-when-should-we-consider-stream-processing-frameworks-in-our-solutions) - [Crypto-secure Data Management with In-Database Blockchain](https://www.wearedevelopers.com/videos/632-crypto-secure-data-management-with-in-database-blockchain) - [What non-automotive Machine Learning projects can learn from automotive Machine Learning projects](https://www.wearedevelopers.com/videos/397-what-non-automotive-machine-learning-projects-can-learn-from-automotive-machine-learning-projects) - [Detecting Money Laundering with AI](https://www.wearedevelopers.com/videos/111-detecting-money-laundering-with-ai) ## Related Articles - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [MLops – Deploying, Maintaining And Evolving Machine Learning Models in Production](https://www.wearedevelopers.com/magazine/115-mlops-deploying-maintaining-and-evolving-machine-learning-models-in-production) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)