MLOps Engineer - Remote
Tasks
- Administer Kubernetes and GKE
- Automate notebook workflows
- Build CI/CD pipelines for machine learning
- Connect machine learning notebooks to big data infrastructure
- Create how to guides for MLOps
- Deploy machine learning on Google Cloud Platform
- Develop MLOps best practices
- Dockerize machine learning scripts
- Implement monitoring and alerting for production workloads
- Manage Kubernetes manifests with Helm
- Manage model registry
- Monitor production data drift
- Optimize cloud compute resources
- Provision and configure public and hybrid clouds
- Schedule ML pipelines
- Set up experiment tracking
- Set up production monitoring and alerts
- Use Bash automation for system integration
- Use infrastructure as code for provisioning and configuration
- Write processed data pipelines to data lakes warehouses and feature stores
Perks/Benefits
Skills/Tech-stack
Alerting | Bash | BigQuery | CI/CD | CircleCI | Cloud Build | Cloud platform | Data Lakes | Data Warehouses | Deployment Manager | Docker | Drift monitoring | Experiment tracking | Feature Engineering | Feature Store | GKE | Go | Google Cloud | Google Cloud Build | Google Cloud Platform | Google Deployment Manager | Helm | Jenkins | Kubernetes | MLOps | Model Registry | Monitoring | Production Drift Monitoring | Python | Statistical Testing | Terraform
Education
N/A
Roles
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