MLOps Engineer

Antwerp, Vlaams Gewest, Belgium February 25, 2026

Creating a robust machine learning infrastructure for production use is a significant hurdle for many of our large-scale clients transitioning towards AI-centric operations. This role presents a unique opportunity for a seasoned MLOps engineer or server-side developer to deepen their expertise in this emerging field and spearhead the formation of our inaugural MLOps team, sharing their knowledge across our organization. In the role of MLOps Engineer, you'll be at the forefront of deploying cutting-edge AI solutions for Faktion’s enterprise clients. Consider a scenario where Faktion’s data scientists have developed a groundbreaking system that can automatically interpret and process thousands of images for a major manufacturing plant. It functions flawlessly in a test environment, but the real challenge lies in its deployment to a production setting. How will this system be scaled to handle millions of images? What’s the best approach for users to interact with this system? What tools or platforms should be utilized for ongoing monitoring? As an MLOps Engineer at Faktion, you will navigate these questions and architect the necessary solutions

Some key responsibilities:

Design and build the machine learning pipelines and cloud infrastructure to support our machine learning systems at scale
Take offline models data scientists build and turn them into a real machine learning production system
Develop and deploy scalable tools and services for our clients to handle machine learning training and inference
Identify and evaluate new technologies to improve performance, maintainability, and reliability of our clients’ machine learning systems
Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
Implement security best practices to safeguard sensitive data and model outputs, and support model development, with an emphasis on auditability, versioning, and data security
Investigate and resolve issues related to model performance, data pipelines, or infrastructure