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.

Find out more about the daily tasks, overall responsibilities, and required experience for this opportunity by scrolling down now.

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. xlxgzvr 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