onsite
MLOps Engineer - Compredict GmbH
MLOps Engineer
Lead end‑to‑end MLOps initiatives, building scalable pipelines with Python, Docker, Kubernetes, and MLflow on AWS, while automating CI/CD workflows and ensuring robust model deployment and monitoring.
About the role
Key Responsibilities
- Design, implement, and maintain production‑grade ML pipelines using Python, Docker, and Kubernetes.
- Integrate MLflow for experiment tracking, model registry, and reproducibility.
- Automate CI/CD workflows for model training, testing, and deployment on AWS.
- Collaborate with data scientists to translate research models into scalable services.
- Monitor model performance, set up alerts, and perform root‑cause analysis for drift.
Requirements
- Strong experience with Python and container orchestration (Docker, Kubernetes).
- Hands‑on knowledge of MLflow, AWS services (SageMaker, ECS, EKS), and CI/CD tools.
- Proficiency in Git, scripting, and automated testing.
- Excellent problem‑solving skills and ability to work cross‑functionally.
- Fluent in English; German is a plus.
Skills
pythondockerkubernetesmlflowawscicd