Machine Learning Engineer

Job Description – Machine Learning Engineer Mission & Context The Machine Learning Engineer plays a key role in enabling the

industrialization of Machine Learning and AI solutions

within the enterprise. The mission of the role is to promote and apply

best practices in production‑ready ML development , ensuring that AI solutions are robust, scalable, monitored, and fully integrated into IT production environments. Machine Learning Engineers bridge the gap between

AI & Analytics teams and IT production , ensuring that Machine Learning models deployed to production are supported by appropriate data pipelines, infrastructure, automation, and monitoring from both a technical and business perspective. They contribute to the full lifecycle of AI services, from design and development to deployment, monitoring, and continuous improvement.

Key Responsibilities Machine Learning Engineers contribute to Machine Learning projects by: Collaborating closely with

Data Scientists

to define and develop solutions that meet business requirements while taking

production constraints

into account (e.g. performance, scalability, latency, data volumes). Supporting the selection of appropriate

infrastructure and serving models , including data ingestion patterns, synchronization models, and API designs (real‑time vs batch processing). Contributing to the

automation and industrialization of ML pipelines , including: Containerization and image creation (Docker / VMs) Preparation of unit, regression, and integration tests CI/CD integration for ML components Supporting Data Scientists in the use of

existing industrial platforms and CI/CD tools

to build, deploy, and monitor AI services. Working closely with

IT Production teams

to support the configuration and parameterization of target environments. Ensuring models in production: Run reliably and without errors Are retrained when required (including automated retraining where applicable) Are monitored from both

IT (technical)

and

business (performance, quality)

perspectives.

Agile & Delivery Context The Machine Learning Engineer typically works in

Agile delivery environments , contributing within cross‑functional teams that combine analytics, engineering, and testing expertise. The role requires close collaboration, continuous feedback, and a strong delivery mindset focused on stable and reusable solutions.

Required Experience & Knowledge Experience Minimum 4 years of relevant experience

as a Machine Learning Engineer, ML Platform Engineer, or similar role

Technical Skills Mandatory Strong experience with

containerization and virtualization

(Docker, VMs) Experience with

AI platforms and development environments CI/CD pipelines, preferably

GitLab CI Code, data, and model versioning practices Advanced

Python

development Package management and dependency management PostgreSQL Preferred Experience integrating systems across different technologies (distributed systems, mainframe environments) Model optimization and compression techniques ELT / ETL tools Big data technologies (e.g.

Apache Spark ) Data flow processing frameworks Data visualization tools

Business & Methodology Mandatory Practical knowledge of

Agile methodologies

Language Requirements English: mandatory Dutch: nice to have French: nice to have

Working Model 50% on‑site / 50% remote

working arrangement

Soft Skills Strong communication skills (verbal and written) Results‑driven with a strong sense of ownership High attention to detail and rigor Creative and analytical problem‑solving mindset Proactive in continuous learning and knowledge sharing Awareness of efficiency and quality of delivery Ability to think beyond existing processes and frameworks Positive, energetic, and collaborative team player Open to change, feedback, and diverse perspectives