Introduction

You take AI models from prototype to production—scalable, robust, and ready for the real world.

Tasks

• You implement and maintain ML pipelines for data ingestion, training and deployment as well as CI/CD processes for models.
• You containerize and package AI applications for hybrid cloud and on-premise environments.
• You deploy models on Kubernetes, Azure and edge target systems and optimize them in terms of latency, accuracy and energy efficiency.
• You implement monitoring, logging, drift detection and A/B testing for production models.
• You ensure reproducibility and experiment tracking throughout the entire model lifecycle, including versioning and metadata management.
• You work closely with AI/ML engineers to make prototypes production-ready.

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Qualifications

• You bring several years of hands-on experience in MLOps or a comparable role, particularly in deploying models on cloud and on-premise infrastructures.
• You have solid Python skills as well as experience with containerization (Docker/Podman), orchestration (Kubernetes) and CI/CD/GitOps workflows (GitLab, Helm charts).
• You have experience with ML lifecycle tools such as experiment tracking, model versioning and monitoring, as well as with deployment considerations for edge and low-power environments.
• You stand out through a strong automation and reliability mindset as well as strong troubleshooting skills at both the infrastructure and model level.
• You are characterized by teamwork skills, communication skills, willingness to learn, analytical thinking and presentation skills.
• Very good German skills and very good English skills (written and spoken) round off your profile.

Contact and further information