onsite
MLOps Engineer - Rheinmetall AG
MLOps Engineer
Lead the design and deployment of scalable machine learning pipelines using Python, Docker, Kubernetes, and AWS. Drive automation, monitoring, and continuous delivery of ML models with CI/CD, MLflow, and Terraform.
About the role
Key Responsibilities
- Design, build, and maintain end‑to‑end ML pipelines from data ingestion to model deployment.
- Containerize models with Docker and orchestrate with Kubernetes for high availability.
- Implement CI/CD workflows for automated testing, packaging, and release of ML artifacts.
- Integrate monitoring, logging, and alerting for model performance and drift detection.
- Collaborate with data scientists, DevOps, and security teams to ensure compliance and scalability.
Requirements
- Proven experience in Python and ML frameworks (TensorFlow, PyTorch, scikit‑learn).
- Hands‑on expertise with Docker, Kubernetes, and cloud services (AWS).
- Strong knowledge of CI/CD pipelines, Git, and automation tools.
- Familiarity with MLflow, Terraform, and model registry best practices.
- Excellent problem‑solving skills and ability to work in a fast‑paced environment.
Skills
pythondockerkubernetesawscicdmlflowterraform