Position: MLOps Engineer
Company: Materialise
Experience required: 5y
English level: upper

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 the deployment, monitoring, 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, at least 5 years of practical use.
- Experience with machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn.
- Proficiency of cloud platforms such as AWS SageMaker (preferred), GCP Vertex AI or Azure ML.
- Familiarity with Docker.
- Familiarity with version control systems (e.g., Git) and CI/CD pipelines.
- Professional English language skills
- Solid knowledge of Linux and Windows operating systems.
- Experience with medical imaging is a plus.