MLOps Engineer
Tasks
- Build automation pipelines for ML platform
- Design AWS cloud architectures
- Implement secure reliable scalable ML services
- Improve MLOps best practices
- Integrate ML models into applications
- Monitor and maintain production ML services
- Operationalize model training and deployment
- Provide tooling for ML workflow reliability maintainability scalability
Perks/Benefits
- N/A
Skills/Tech-stack
API deployment | AWS | AWS CDK | AWS ECS | Apache Airflow | CI/CD | CircleCI | Cloud platform | Containerization | Data Drift | Data drift monitoring | Docker | Docker containers | Drift monitoring | Experiment tracking | FastAPI | GitHub Actions | Google Cloud | Google Cloud Platform | Infrastructure as Code | Kubeflow | Kubernetes | MLOps | MLflow | Machine Learning | Metaflow | Microsoft Azure | Model Deployment | Model Drift | Model Registry | Model Training | Model drift monitoring | Model versioning | Networking Security | Python | Terraform | “as-code”
Education
N/A
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