Machine Learning Engineer

Machine Learning Engineer

Location : Brussels, Belgium

Contract Duration : 8 months

Work Mode : Hybrid

Overview :

The ML Engineer is responsible for designing, building, deploying and maintaining machine learning models and ML pipelines within Client. 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-premise.

The ML Engineer works closely with data engineers, developers, architects and business stakeholders, translating ML use cases into sustainable technical implementations that are in line with the applicable regulations (in particular 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.
• Monitor data quality, consistency, and reproducibility within ML workflows.
• Collaborate with relevant teams to make data available for ML use cases in a reliable and reusable way.

Model development and validation

• Design, train, test, and tune machine learning models for use cases such as classification, regression, forecasting, discovery, or scoring.
• Selecting suitable techniques and evaluation methods according to the use case and production context.
• Conducting experiments and benchmarking models with attention to quality, explainability and maintainability.
• Define clear validation criteria for models before they are put into production.

Operationalizing ML solutions

• Translating 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

• Establish and maintain ML pipelines, CI/CD processes, and release approach for ML components.
• Provide 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 reliability, scalability, cost control and operational manageability of ML solutions.

Collaboration and knowledge sharing :

• Coordinating with developers, data engineers, architects and business stakeholders about technical choices and implementation.
• Contribute to good practices on ML engineering, testing, deployment and monitoring within the client premises.
• Documenting implementations, assumptions ad operational points of attention.
• Sharing knowledge with teams and actively contributing to the maturity of ML within the organization

Language skills

:

• French or Dutch speaking
• Understanding the 2nd national language

Working regime:

Hybrid, in particular 2 days in the office and 3 days teleworking every week