ML Platform Engineer
Tasks
- Architect machine learning platforms
- Build MLOps platforms for full lifecycle
- Build infrastructure as code
- Collaborate with data scientists and engineers
- Deploy models with Docker and Kubernetes
- Design automated pipelines for data processing model training and deployment
- Develop tools for data scientists to use ML platform
- Enable version control for code data and models
- Ensure scalability reliability security and compliance
- Implement cloud ML services integration
- Implement monitoring, logging, and alerting
- Optimize platform performance cost and stability
- Set up CI/CD pipelines
Perks/Benefits
Skills/Tech-stack
AWS | Azure | CI/CD | Cloud platform | Compliance | Data Pipelines | Docker | ETL | Google Cloud | Google Cloud Platform | Infrastructure as Code | Kubernetes | Logging | MLOps | Machine Learning | Model Deployment | Model Monitoring | Monitoring | Observability | Python | Security | Terraform | “as-code”
Education
N/A
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