Machine Learning Operations Engineer (MLOps)
Tasks
- Build Docker containers for machine learning models
- Deploy Docker container images on Kubernetes
- Deploy machine learning models as REST APIs on cloud infrastructure
- Deploy the platform to production on cloud infrastructure
- Develop Flask and FastAPI model APIs
- Develop end to end backend pipelines for deploying machine learning models
- Develop end to end pipelines for backend infrastructure
- Document processes code reviews and workflows
- Enhance platform performance with continuous unit testing
- Implement unit and stress testing frameworks for production services
- Integrate SQL database objects with the software platform
- Integrate machine learning model REST API endpoints with the platform
- Maintain Kubernetes and virtual machines on Google Cloud Platform
- Maintain and optimize database systems for performance and cost
- Monitor and measure product performance
- Monitor cloud infrastructure and logs for usage and vulnerabilities
- Review performance data to develop standard work
Perks/Benefits
- N/A
Skills/Tech-stack
Cloud platform | Continuous integration | Docker | FastAPI | Flask | Google Cloud | Google Cloud Platform | Kubernetes | Machine Learning | Machine learning deployment | Python | REST APIs | SQL | Stress Testing | Unit Testing
Education
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