onsite
MLOps Engineer - HUK-COBURG VVaG
MLOps Engineer
Lead the deployment and scaling of machine learning models using Python, AWS, Docker, and Kubernetes, ensuring robust CI/CD pipelines and continuous monitoring for high‑availability production environments.
About the role
Key Responsibilities
- Design, build, and maintain end‑to‑end ML pipelines from data ingestion to model serving on AWS infrastructure.
- Containerize models with Docker, orchestrate deployments with Kubernetes, and automate releases via CI/CD pipelines.
- Implement monitoring, logging, and alerting for model performance and infrastructure health.
- Collaborate with data scientists to translate research prototypes into production‑ready services.
- Optimize resource usage and cost through autoscaling, spot instances, and efficient architecture choices.
Requirements
- Proven experience with Python, ML frameworks (TensorFlow, PyTorch, scikit‑learn) and model deployment.
- Hands‑on expertise in AWS services (SageMaker, ECS/EKS, Lambda, CloudWatch, S3).
- Strong knowledge of Docker, Kubernetes, Helm, and CI/CD tools (GitHub Actions, Jenkins, ArgoCD).
- Solid understanding of infrastructure as code (Terraform, CloudFormation) and Git workflow.
- Excellent problem‑solving skills, ability to work independently and in cross‑functional teams.
Skills
pythonmachine learningawsdockerkubernetescicd