MLOps Engineer (m/w/d)

Job summary

The MLOps Engineer role focuses on deploying AI models into production environments, ensuring they are scalable, robust, and ready for real-world applications. Key responsibilities include implementing and maintaining ML pipelines, containerizing AI applications for cloud and on-premise environments, and optimizing models on Kubernetes, Azure, and Edge systems. The position requires strong Python skills and experience with CI/CD processes, monitoring, and ML lifecycle tools.

Qualifications

• Several years of practical experience in MLOps or a comparable role
• Strong Python skills
• Experience with containerization (Docker/Podman)
• Experience with orchestration (Kubernetes)
• Experience with CI/CD/GitOps workflows (GitLab, Helm charts)
• Experience with ML lifecycle tools such as experiment tracking, model versioning, and monitoring
• Experience with deployment considerations for edge and low-power environments
• Strong automation and reliability mindset
• Strong troubleshooting skills across infrastructure and model levels
• Teamwork, communication, willingness to learn, analytical thinking, and presentation skills
• Very good German and English language skills, both written and spoken

Responsibilities

• Implement and maintain ML pipelines for data ingestion, training, and deployment
• Containerize and package AI applications for cloud and on-premise environments
• Deploy models on Kubernetes, Azure, and Edge systems and optimize them
• Implement monitoring, logging, drift detection, and basic A/B testing
• Ensure reproducibility and experiment tracking throughout the model lifecycle
• Collaborate closely with AI/ML engineers

Skills

Relocation