MLOps & AI Platform Engineer
Tasks
- Build and automate machine learning pipelines
- Create CI/CD pipelines for AI workloads
- Deploy and manage ML workloads on Kubernetes
- Design and maintain MLOps platforms
- Ensure security, scalability, and reliability
- Implement infrastructure-as-code
- Implement model versioning and model registry
- Monitor model performance and platform health
- Troubleshoot production issues and optimize performance
Perks/Benefits
- N/A
Skills/Tech-stack
AI Pipelines | Alerting | Amazon Web Services | Apache Airflow | Bash | CI/CD | Cloud platform | Docker | Experiment tracking | GitHub Actions | Google Cloud | Google Cloud Platform | Infrastructure as Code | Kubeflow | Kubernetes | Logging | MLOps | MLflow | Microsoft Azure | Model Registry | Monitoring | Observability | Prometheus | Python | Sagemaker Pipelines | Terraform | Vertex AI | Vertex AI pipelines | Web Services | “as-code”
Education
Regions
Countries
States
Cities
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