Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

About the Role

Ensure reliable, efficient, and scalable machine learning solutions in production. Bridge the gap between model development and enterprise deployment, creating the technical foundation for sustainable, automated model operations. Focus on industrializing ML workloads by designing end-to-end MLOps architectures, automating pipelines, and establishing standards for monitoring, quality assurance, and governance. Collaborate with data scientists, AI engineers, and cloud/platform teams to ensure data-driven solutions are innovative, maintainable, secure, and economically viable.

Responsibilities

Design and operate scalable MLOps pipelines, covering data integration, model training, deployment, and monitoring.
Automate CI/CD processes for machine learning models and ensure reproducible workflows.
Manage versioning of data, models, and artifacts, including full lifecycle management.
Implement monitoring and alerting to track model performance, stability, and data drift.
Optimize ML infrastructure for scalability, performance, and cost efficiency.
Integrate ML services into existing platforms and enterprise systems via APIs.
Collaborate closely with cloud and DevOps teams and contribute to ML architecture decisions.
Ensure security, privacy, and compliance requirements are met in production environments.
Support the transition from proof-of-concept solutions to enterprise-grade production systems.
Promote best practices in MLOps, automation, and governance across international teams.

Requirements

Several years of experience in MLOps, DevOps, or operating machine learning systems.
Strong expertise in CI/CD, containerization (e.g., Docker, Kubernetes), and automation.
Hands-on experience with cloud platforms such as AWS, Azure, or GCP.
Solid understanding of machine learning workflows and common ML frameworks.
Experience with monitoring, logging, and observability tools.
Knowledge of infrastructure-as-code (e.g., Terraform).
Experience with version and artifact management in ML environments.
Structured, analytical, and solution-oriented working style.
Experience working in international projects or team environments.
Excellent German and English language skills.
Willingness to travel occasionally (limited frequency).

Experience Level

Senior