Assignment Context:

RSVZ is a dynamic organization where more than 150 people work within the Informatics department. RSVZ is a bilingual environment, with both French-speaking and Dutch-speaking stakeholders. The organizational culture is informal. Within IT, Agile SAFe (Scaled Agile Framework) is applied, and the development teams are multidisciplinary.

At RSVZ, we aim to deploy machine learning solutions to optimize processes, better support decision-making, and strengthen digital services. The focus is on reliable, scalable, and maintainable ML solutions that can be effectively integrated into existing systems and data flows, both in an Azure environment and on-premises.

We are looking for an ML Engineer who will be responsible for building and operationalizing machine learning solutions in production. The role is situated at the intersection of data, software engineering, and MLOps, with a clear focus on robust implementation, reproducibility, monitoring, regulatory compliance, and continuous improvement.

Role

The ML Engineer is responsible for designing, building, deploying, and maintaining machine learning models and ML pipelines within RSVZ. He/she ensures that models are not only performant in experiments, but also function reliably, scalably, and manageably in production, both on cloud infrastructures (Azure) and on-premises.

The ML Engineer works closely with data engineers, developers, architects, and business stakeholders, and translates ML use cases into sustainable technical implementations that comply with applicable regulations (notably the European AI Act).

Key Responsibilities:

Data preparation and feature engineering

• Processing, analyzing, and preparing data from various internal and external sources.
• Designing and implementing data transformations and feature engineering processes.
• Safeguarding data quality, consistency, and reproducibility within ML workflows.
• Collaborating with relevant teams to make data reliably and reusable available for ML use cases.

Model development and validation

• Designing, training, testing, and tuning machine learning models for use cases such as classification, regression, forecasting, detection, or scoring.
• Selecting suitable techniques and evaluation methods based on the use case and production context.
• Conducting experiments and benchmarking models with attention to quality, explainability, and maintainability.
• Defining clear validation criteria for models before they are put into production.

Operationalizing ML solutions

• Translating models and experiments into production-ready services and pipelines.
• Integrating models into backend services, APIs, or batch processes.
• Implementing version control for code, configuration, models, and relevant datasets.
• Contributing to a standardized and reliable deployment approach for ML solutions.

MLOps, monitoring, and reliability

• Setting up and maintaining ML pipelines, CI/CD processes, and release approaches for ML components.
• Providing monitoring for performance, stability, latency, error handling, data drift, and model drift.
• Developing retraining and feedback mechanisms to keep models up-to-date and performant.
• Ensuring the reliability, scalability, cost control, and operational manageability of ML solutions.

Collaboration and knowledge sharing

• Coordinating with developers, data engineers, architects, and business stakeholders on technical choices and implementation.
• Contributing to best practices regarding ML engineering, testing, deployment, and monitoring within RSVZ.
• Documenting implementations, assumptions, and operational considerations.
• Sharing knowledge with teams and actively contributing to the maturity of ML within the organization.

Behavioral:

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

Language skills:

• French or Dutch-speaking
• Able to understand the second national language

Work regime:

Hybrid, specifically 2 days per week at the office and 3 days working remotely