MLOps Engineer
Tasks
- Build ML platform components model registries experiment tracking feature stores
- Define and enforce MLOps standards and documentation
- Deploy ML models using APIs microservices and containers
- Develop end-to-end ML pipelines
- Implement CI CD pipelines for ML assets
- Implement DevSecOps best practices
- Monitor model performance system health and data drift
- Perform root cause analysis of ML performance degradation
- Troubleshoot pipeline and infrastructure issues
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Azure | CI/CD | CloudFormation | CloudWatch | Data Drift | Data Drift Detection | Datadog | Docker | Drift Detection | ELK Stack | Experiment tracking | Feature Store | GCP | GitHub Actions | GitLab CI | Grafana | Infrastructure as Code | Jenkins | Kubernetes | MLOps | Machine Learning | Model Monitoring | Model Registry | Prometheus | PyTorch | Python | Scikit-learn | TensorFlow | Terraform | XGBoost | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
Roles
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