Werkstudent MLOps-Plattform & Cloud-Native Infrastruktur (m/w/d)
Tasks
- Automate build and deployment steps
- Build ML platform components
- Build and maintain Docker images
- Build training and serving pipelines
- Configure networking storage and access
- Create unit, integration, and end-to-end tests
- Deploy components with Helm and Kustomize
- Develop infrastructure as code architecture
- Document architecture deployment and runbooks
- Implement service interfaces with REST and GraphQL
- Integrate Kafka compatible event streaming for inference logging
- Integrate software infrastructure and ML components
- Maintain CI/CD pipelines
- Manage Kubernetes cluster deployments
- Manage MLflow experiment tracking and model registry
- Manage container images in registry
- Operate Kubeflow Pipelines and Training Operator
- Prepare demo presentation and reproducible materials
- Register deploy and version ML models
- Serve models with KServe
- Store artifacts with MinIO S3 compatible storage
- Troubleshoot cluster issues with kubectl
Perks/Benefits
Skills/Tech-stack
CI/CD | CloudEvents | Docker | Git | GraphQL | Helm | Infrastructure as Code | Integration Testing | KServe | Kafka | Kubectl | Kubeflow | Kubeflow Pipelines | Kubernetes | Kustomize | Linux | MLflow | Message Queues | Minio | OpenAPI | OpenStack | OpenTofu | Python | REST | Smoke Testing | Terraform | Unit Testing | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
DevOps | DevOps Engineer | Engineer | ML Engineer | MLOps Engineer
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