Machine Learning Engineers at this financial-services organisation build production-ready ML pipelines and integrate models into existing IT environments to deliver automated AI services. The role focuses on production deployment, CI/CD with GitLab, containerization (Docker/VM), and advanced Python development to ensure models run reliably and are monitored in production.
The mission
The data engineering and AI team maintains multiple ML services that support scoring, fraud detection and customer insights across high-volume batch and near-real-time workflows. The technical landscape includes Python-based model code, PostgreSQL for feature and metadata storage, container images for deployment, and GitLab CI pipelines for automated testing and release. Delivering robust model versioning and data versioning is a priority so models can be audited and retrained predictably.
On a day-to-day basis you will work with data scientists, analytics engineers and IT production to choose the right serving approach, implement data ingestion and feature pipelines, and package models for deployment. You will automate tests and CI/CD, help configure the production environment, and set up monitoring and retraining triggers so models remain accurate and operational. Typical collaboration involves a cross-functional team of data scientists and platform engineers and regular handoffs to production support.
Your responsibilities
- Implement production-grade ML pipelines that enable reproducible training, testing and deployment, focusing on reliability and traceability
- Integrate model serving with production infrastructure using containerization and infrastructure parameterisation
- Design and enforce model, code and data versioning workflows to support audits and automated retraining
- Build and maintain GitLab CI pipelines for unit, integration and regression testing of ML components
- Collaborate with data scientists to optimise model packaging and to apply model compression where needed for performance constraints
- Monitor deployed models and data quality, and implement automated retraining or alerting when performance degrades
Your profile
Essential skills
- Minimum 4 years of professional experience in machine learning engineering or related roles
- Strong Python development skills with experience in packaging and dependency management
- Practical experience with containerization and virtualisation (Docker, VM image creation)
- Hands-on experience with CI/CD, specifically GitLab CI for automated testing and deployments
- Familiarity with code, model and data versioning practices and tools
- Operational knowledge of PostgreSQL for feature or metadata storage
- Comfortable working in agile teams and translating data-science work into production deliverables
Preferred skills
- Experience integrating with heterogeneous systems including legacy or distributed platforms
- Knowledge of ETL/ELT tooling and big-data processing (Spark)
- Familiarity with model compression techniques and data flow processing
- Experience with data visualisation tools for monitoring model behaviour
Languages
- English, C1 (mandatory)
- Dutch, B1 (plus)
- French, B1 (plus)
Education
- Degree in Computer Science, Data Science, Engineering or equivalent professional experience