MLops Engineer

Responsibilities

• Collaborate closely with data scientists and data engineers to design and run machine learning experiments efficiently.
• Design, build, and maintain ML workflows for model training, validation, and deployment.
• Implement practices to monitor model performance, data inputs, and respond accordingly.
• Troubleshoot and resolve issues in machine learning systems.
• Develop tools, libraries, frameworks, and services to support machine learning needs.
• Establish and manage data platforms and data ingestion pipelines.
• Automate processes to monitor and improve model and system performance.
• Contribute to the development of the team’s capabilities and expertise in line with emerging technologies and trends.

Requirements:

• Bachelor’s degree in computer science, Engineering or a related field, or equivalent experience.
• Experience with machine learning lifecycle, including model selection, training, validation, and deployment.
• Strong understanding of machine learning algorithms and principles.
• Hands-on experience with Azure ML, Git, and SonarQube.
• Proficiency with Python, and experience with machine learning libraries.
• Experience with Azure cloud services and understanding of distributed systems.
• Knowledge of MLFlow data pipeline tool.
• Familiarity with DevOps practices, such as CI/CD, containerization (Docker), and orchestration (Kubernetes). Terraform experience is a plus.
• Strong problem-solving skills, attention to detail.
• Excellent communication and presentation skills and the ability to work well in a team.
• Fluent in English, additional language skills in German, Dutch or French is a plus.