MLOps Engineer

Materialise

Leuven or Barcelona

As an MLOps Engineer, you will play a critical role in bridging the gap between machine learning research prototypes and production deployment. You will be responsible for designing, implementing, and maintaining the infrastructure and tools necessary to streamline our research and management of machine learning models. Your expertise in machine learning, software engineering, and cloud platforms will be invaluable as we scale our machine learning operations.

What you will do

Transform proof-of-concept scripts into Pipelines with different processing steps;
Develop and maintain monitoring and alerting systems to ensure the health and performance of deployed models;
Cross-functional collaboration with research teams and development teams;
Maintain scalable, robust, and reliable infrastructure for model training, testing, deployment, and monitoring;
Optimize and enhance model performance, scalability, and reliability in production environments;
Take responsibility for code testing and quality checking;
Stay on top of the latest trends in the field of MLOps and cloud platforms.

Your profile

Bachelor's or Master's degree in Computer Science, Data Engineering, Biomedical Engineering, or a related field;
Strong programming skills in Python;
Experience with machine learning frameworks such as TensorFlow, PyTorch, ONNX, or scikit-learn;
Proficiency in cloud platforms such as AWS SageMaker (preferred), GCP Vertex A,I or Azure ML;
Experience with medical imaging is a plus;
Knowledge of experiment tracking frameworks like mlflow or weights & biases;
Familiarity with Docker;
Familiarity with version control systems (e.g., Git, Git-LFS, DVS) and CI/CD pipelines;
Experience with Terraform;
Professional English language skills;
Solid knowledge of Linux and Windows operating systems.