Senior MLOps Engineer (m/w/d)

Job summary

The role brings machine-learning models from prototype to secure production, including power-constrained and air-gapped environments. It owns end-to-end ML pipelines, CI/CD and model-release processes, containerized deployment across cloud and on-premise infrastructure, and operational practices such as monitoring, drift detection, A/B testing, and experiment tracking. The role also mentors engineering colleagues and coordinates production rollouts.

Qualifications

• Several years of experience in MLOps or production ML engineering with end-to-end delivery responsibility.
• Practical experience with containerization, Kubernetes or OpenShift, Azure, AWS or GCP, GitOps, and CI/CD.
• Expertise in ML monitoring and observability, model versioning, and experiment tracking.
• Experience with edge-deployment requirements.
• Strong system-design, automation, and operational-leadership skills.
• Very good spoken and written German and English.

Responsibilities

• Design and own end-to-end ML pipelines, CI/CD, and model-release processes.
• Manage containerization, deployment strategies, and hybrid cloud and on-premise model orchestration.
• Implement monitoring, drift detection, A/B testing, and operational model-performance and reliability KPIs.
• Ensure reproducibility, metadata and experiment tracking at scale, and secure, compliant deployments.
• Mentor MLOps and engineering colleagues, conduct architecture reviews, and coordinate cross-team production rollouts.
• Optimize models and runtime environments for air-gapped and power-constrained deployments.

Skills

Languages

Work schedule

Contract length

Relocation