(US) Principal ML System Engineer
Tasks
- Define experiment tracking standards
- Define feature store architecture
- Define model registry standards
- Define reference architectures and standards for scalable ML pipelines
- Define security architecture for ML platform
- Define technical vision and strategy for ML platform
- Design secure cost efficient integration patterns with APIs and data sources
- Drive CI CD for models and ML pipelines
- Ensure authentication and role based access control adoption
- Establish reliability observability and performance practices for ML systems
- Implement audit logging and compliance monitoring
- Implement monitoring, alerting, and automated remediation
- Lead mentorship and technical guidance for engineering teams
- Set MLOps best practices for models and workflows
- Translate business objectives into technical roadmap
Perks/Benefits
- N/A
Skills/Tech-stack
AWS | Access Control | Alerting | Audit Logging | Authentication | Automated remediation | Azure | CI/CD | Cloud platform | Compliance Monitoring | Containerization | Cost Optimization | Docker | Experiment tracking | Feature Store | Google Cloud | Google Cloud Platform | Java | Kubeflow | Kubernetes | MLOps | MLflow | Machine Learning | Model Registry | Model Serving | Monitoring | Observability | Performance optimization | Python | Ray | Role-Based Access | Role-Based Access Control | Security Architecture
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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