Machine Learning Engineer

• Data preparation and feature engineering: Process, analyze, and prepare data from various internal and external sources. Design and implement data transformations and feature engineering processes. Ensure data quality, consistency, and reproducibility within ML workflows. Collaborate with relevant teams to make data reliably and reusable for ML use cases.
• Model development and validation: Design, train, test, and tune machine learning models for use cases such as classification, regression, forecasting, detection, or scoring. Select appropriate techniques and evaluation methods based on the use case and production context. Conduct experiments and benchmark models with attention to quality, explainability, and maintainability. Define clear validation criteria for models before they are put into production.
• Operationalizing ML solutions: Translate models and experiments into production-ready services and pipelines. Integrate models into backend services, APIs, or batch processes. Implement version control for code, configuration, models, and relevant datasets. Contribute to a standardized and reliable deployment approach for ML solutions.
• MLOps, monitoring, and reliability: Set up and maintain ML pipelines, CI/CD processes, and release approaches for ML components. Provide monitoring for performance, stability, latency, error handling, data drift, and model drift. Develop retraining and feedback mechanisms to keep models current and performant. Ensure reliability, scalability, cost control, and operational manageability of ML solutions.
• Collaboration and knowledge sharing: Coordinate with developers, data engineers, architects, and business stakeholders on technical choices and implementation. Contribute to best practices around ML engineering, testing, deployment, and monitoring. Document implementations, assumptions, and operational considerations. Share knowledge with teams and actively contribute to the maturity of ML within the organization.

• Results-oriented and pragmatic: Able to translate ML solutions into stable and usable production components.
• Strong analytical and logical thinking skills.
• Quality-conscious, with attention to reliability, maintainability, and clarity.
• Ownership of technical implementations and proactive in proposing improvements.
• Communicative: Can clearly explain technical choices to both technical and non-technical stakeholders.
• Strong collaboration within multidisciplinary teams.
• Eager to learn and motivated to apply new techniques and best practices in a production context.

Language Skills:

• Fluent in French or Dutch
• Understanding of the second national language, * Azure (nice to have) - Level: Junior - Most recent: Any time
• C# / .NET / Blazor Framework - Level: Junior - Most recent: Any time
• CI/CD, versiebeheer en deployment van ML-services - Level: Junior - Most recent: Any time
• Containerisatie en deployment patterns (Docker) - Level: Junior - Most recent: Any time
• Datavoorbereiding, feature engineering en modelvalidatie - Level: Junior - Most recent: Any time
• Experiment tracking, model registry of workflow orchestrationExperiment tracking, model registry of - Level: Junior - Most recent: Any time
• Integratie van ML-componenten in applicaties of backend-services - Level: Junior - Most recent: Any time
• Machine learning libraries and opensource model/tools (scikit-learn, PyTorch,, Langraph, Ollama, Lan - Level: Junior - Most recent: Any time
• ML-pipelines en MLOps-praktijken (Azure Devops) - Level: Junior - Most recent: Any time
• Monitoring van modellen en pipelines (logging, metrics, drift-detectie, opentelemetry, DynaTrace) - Level: Junior - Most recent: Any time
• Python (data- en ML-development) - Level: Confirmed - Most recent: Any time
• SQL en dataverwerking in productiecontext - Level: Junior - Most recent: Any time

Language requirements:

Dutch or French Level Native