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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Product Owner (ML & GenAI) for Operations - **Company:** Danone Nederland B.V. - **Location:** Amsterdam, Netherlands (Remote available) - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Computer Animation, Artificial Intelligence, Amazon Web Services, Microsoft Azure, C Sharp (Programming Language), Cloud Database, Computer Programming, Software Debugging, Python (Programming Language), Machine Learning, Software Product Management, Azure Machine Learning, Feature Engineering, Model Validation, Backend, Information Technology, Software Coding, Unsupervised Learning, Databricks - **Published:** September 8, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=a91a78134fad5f45 ## About the Role * Product ownership in a technical domain: roadmap, backlog, prioritization, stakeholder management, and the ability to say no. Comfortable as the single owner of an AI product, not as a pure project coordinator. * Solid ML foundation: supervised/unsupervised learning, time-series and anomaly detection, feature engineering, model evaluation, and the judgement to choose the right model for the loss - not the most fashionable one. * Hands-on with data: exploratory analysis, industrial/OT data (sensors, tags, historians), data quality, and working with process engineers on loss trees and critical equipment. * GenAI literacy: foundation models, orchestration (e.g. LangChain / similar), and when GenAI is the right tool vs classical ML. * Proficiency in at least one language used for data/ML work (Python preferred; Java/C# a plus). Comfortable writing code, not only slides. * Experience with cloud data/ML platforms (preferably Azure, Databricks). * Change management and user empathy: can work with operators, maintainers, supply chain, procurement and leadership from idea through go-live. * Value tracking: can turn a use case into a measurable business case and keep score after deployment. * Strong collaboration and communication: explain models and trade-offs to non-technical stakeholders; work with IT, Data, OT, and partners. * Innovative but pragmatic: experiment fast, stay current, ship what plants will use. * Fluent English. About You: * Bachelor's or Master's in Computer Science, Engineering, Data Science, Industrial Engineering, or a related field. PhD is a plus; equivalent hands-on experience equally valued. * Professional experience in IT/Data/AI, with a mix of machine learning delivery and product ownership (or a technical lead who has owned a product end to end). * Demonstrated experience taking an ML or AI solution from idea to users in a live operational environment - including messy data, model iteration, and adoption - not only a notebook or a vendor demo. * Experience with industrial or operations context is a strong plus (manufacturing, maintenance, process, OT/IT). * Experience working with cross-functional teams, plants/sites, and external partners. * Experience with cloud platforms (Azure, AWS, or GCP) and with programming/frameworks used in ML and backend development. * Project/product delivery track record: multiple workstreams, clear priorities, on-time delivery. ## Description * Act as Product Owner for AI projects and products in Operations: vision, roadmap, backlog, and delivery with factories, functions, zones, IT, OT, and external partners. * Understand the mechanics behind the product (data, models, architecture, workflow) - and the process around it. * Be hands-on: work with data, build and evaluate models, and prototype solutions, not only coordinate others. * Take use cases from ideation to deployment: change management, user coaching, and value tracking against the business case. * Deliver PoCs, scale what works, kill what does not, and build in-house ML/GenAI capability. * Define and run sustainable AI development and management practices in line with Responsible AI. Product ownership * Own the product roadmap for assigned AI products (ML platforms and GenAI solutions) and keep it aligned with Operations priorities and the business case. * Translate business problems into use cases, data needs, model choices, and a sequenced backlog. * Run the product with cross-functional teams: process / IWS, OT, data, IT, factories, and partners. Decide what is in, what is out, and when it is good enough to go live. * Set acceptance criteria, go-live criteria, and model-evaluation standards. Challenge black-box delivery until mechanics and quality are understood. Hands-on ML / data / GenAI * Work with industrial data: tags, historians, time series, data gaps, cleansing, feature engineering. * Build, train, evaluate, and iterate models (anomaly detection, predictive / remaining-useful-life style use cases, classical ML, and GenAI). * Ensure a consistent, state-of-the-art architecture across products; experiment with new ML/GenAI techniques and bring them into the stack (Azure / Databricks and related tools). * Deliver in-house solutions and integrations; test, troubleshoot, and debug until they work in a plant context. Users, change, value * Work with users from ideation to deployment: workshops, shadowing on the line, demos, training, hypercare. * Drive change management so alerts and tools are used - not only installed. Coach key users and application owners. * Track value (e.g. unplanned downtime, PR, quality, cost) versus the business case; feed results back into roadmap and model priorities. * Document learnings and best practices so successful products can be scaled to further sites/lines. Lab / CoE * Animate the centre of excellence and AI self-service where it helps adoption. * Mentor others and grow in-house capability so Operations is not dependent on a single expert or vendor. Indicative KPIs * Roadmap delivery: committed site/line onboardings and product increments on time. * Model / product quality: agreed evaluation criteria met before go-live; alert precision/usefulness in operation. * Adoption: share of modelled assets / target users actually using the product (actioned alerts, active companions). * Value: realized vs business-case impact , with a clear measurement method. * Lab throughput: PoCs taken to a working product or explicitly stopped, with documented learnings (indicative ~4-5 prototypes/year, 2-3 months each). Skills needed for the role ## Related Videos - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Fully Orchestrating Databricks from Airflow](https://www.wearedevelopers.com/videos/336-fully-orchestrating-databricks-from-airflow) - [Tomorrow's cloud data platforms - fully managed database-as-a-service (DBaaS)](https://www.wearedevelopers.com/videos/254-tomorrow-s-cloud-data-platforms-fully-managed-database-as-a-service-dbaas) - [Building Products in the era of GenAI](https://www.wearedevelopers.com/videos/827-building-products-in-the-era-of-genai) - [Databaseless Data Processing - High-Performance for Cloud-Native Apps and AI](https://www.wearedevelopers.com/videos/1024-databaseless-data-processing-high-performance-for-cloud-native-apps-and-ai) - [Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source](https://www.wearedevelopers.com/videos/100151-cutting-llm-costs-without-cutting-quality-how-to-beat-proprietary-llms-with-fine-tuned-open-source) ## Related Articles - [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) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Best Companies in the Netherlands: Top 25 Companies in 2023 ](https://www.wearedevelopers.com/magazine/193-best-companies-in-the-netherlands-top-25-companies-in-2023) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts)