onsite
MLOps Engineer - Huk Coburg
MLOps Engineer
Lead the design and maintenance of scalable ML pipelines, deploying models to production environments using Docker, Kubernetes, and CI/CD workflows on AWS, while ensuring robust monitoring and continuous improvement of data science workflows.
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, ensuring high availability and scalability.
- Implement CI/CD pipelines for automated testing, validation, and deployment of ML artifacts.
- Integrate monitoring and logging solutions (Prometheus, Grafana, ELK) to track model performance and drift.
- Collaborate with data scientists, DevOps, and security teams to enforce best practices and compliance.
Requirements
- Proven experience in MLOps or related roles, with strong Python programming skills.
- Hands‑on expertise with Docker, Kubernetes, and cloud platforms (AWS preferred).
- Familiarity with CI/CD tools (GitLab CI, Jenkins, ArgoCD) and configuration management (Terraform, Ansible).
- Knowledge of MLflow or similar model management frameworks.
- Excellent problem‑solving skills and a collaborative mindset.
Skills
pythondockerkubernetescicdmlflowawsterraform