MLOps Engineer
Job Description:
As an MLOps Engineer Consultant, you play a vital role in bridging the gap between data science and production environments. You ensure that machine learning models are robust, scalable, and seamlessly integrated into business applications.
You’ll collaborate with data scientists, cloud engineers, and product teams to deploy, monitor, and maintain AI solutions in a reliable and secure way.
Responsibilities:
Design, build, and maintain scalable ML infrastructure and CI/CD pipelines.
Automate the deployment and monitoring of machine learning models in production environments.
Work closely with data science teams to ensure smooth model handoff and reproducibility.
Monitor performance, manage versioning, and troubleshoot production issues related to ML models.
Implement best practices for model lifecycle management, including governance, logging, and testing.
Ensure compliance with data security and privacy regulations throughout the ML workflow.
Stay up to date with MLOps tools, frameworks, and cloud technologies to continuously improve processes.
Qualifications:
Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.
Hands-on experience with MLOps tools (e.g., MLflow, Kubeflow, DVC, Airflow) and containerization (Docker, Kubernetes).
Proficient in cloud platforms (Azure, AWS, or Google Cloud) and infrastructure-as-code (e.g., Terraform).
Strong understanding of CI/CD principles, DevOps practices, and ML model deployment strategies.
Programming skills in Python and familiarity with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
Excellent communication skills and ability to collaborate across teams.
Based in Belgium and fluent in Dutch.
