An organisation with an established AI and Analytics community is industrialising the way machine learning services are developed, deployed, and monitored. As a Senior Machine Learning Engineer, you will turn data science solutions into production-ready services using advanced Python, containerisation, and CI/CD practices.
The mission
Machine Learning Engineers define and apply development standards across the ML and AI community. The work covers complete machine learning pipelines, including data quality checks, data flow design, model integration, deployment, and monitoring. You will help ensure that AI services can meet business requirements such as real-time responses and defined processing volumes. The technical landscape includes Docker or virtual machine images, AI platforms and IDEs, GitLab CI, code, model and data versioning, and PostgreSQL.
You will work between Data Scientists and IT Production, translating model requirements into deployable and supportable services. Your scope includes selecting suitable run infrastructure and serving models, automating pipeline components, preparing unit, regression, and integration tests, and supporting environment configuration. You will also help ensure that models run reliably, are retrained when required, and are monitored from both technical and business perspectives.
Your responsibilities
- Design production-oriented machine learning pipelines with Data Scientists, taking ingestion patterns, API synchronicity, processing volumes, and operational constraints into account.
- Automate pipeline components and deployment processes through container or virtual machine images, testing, versioning, and CI/CD workflows.
- Integrate AI services into production environments and coordinate technical requirements with IT Production and other infrastructure teams.
- Implement data quality checks, model monitoring, retraining mechanisms, and controls that support reliable business use of deployed models.
- Support Data Scientists in using industrial AI platforms, development environments, package management tools, and dependency management practices.
- Document technical decisions and communicate clearly with AI, analytics, development, and operations stakeholders.
Your profile
Essential skills
- At least 4 years of relevant experience as a Machine Learning Engineer or in a closely related engineering role.
- Ability to develop maintainable production code with advanced Python.
- Practical experience with containerisation or virtualisation, including Docker or comparable technologies.
- Experience using AI platforms and IDEs to develop, package, deploy, or monitor machine learning services.
- Capability to build and maintain CI/CD pipelines, including GitLab CI.
- Experience with code, model, and data versioning, as well as package management and dependency management.
- Working knowledge of PostgreSQL and data pipeline concepts.
- Knowledge of agile methodology, with clear written and oral communication across technical teams.
- A rigorous, results-oriented approach to delivery, troubleshooting, continuous learning, and constructive collaboration.
Preferred skills
- Experience integrating distributed or mainframe technologies with infrastructure components.
- Knowledge of model compression techniques and data flow processing.
- Experience with ELT or ETL tools, Apache Spark, or other big data tools.
- Familiarity with data visualisation tools.
Languages
- English: mandatory, professional working proficiency.
- Dutch: a plus.
- French: a plus.