Principal ML System Engineer
Tasks
- Build MLOps practices and standards
- Create multi quarter platform roadmap
- Create secure infrastructure integration patterns
- Define ML security architecture and access control
- Define machine learning platform technical vision
- Design audit logging and compliance monitoring
- Design reference architectures for ML pipelines
- Develop model registry and feature store patterns
- Drive secure scalable cost efficient ML infrastructure
- Establish experiment tracking
- Implement model CI CD
- Implement production monitoring alerting remediation
- Lead distributed ML workloads performance optimization
- Provide technical leadership across engineering teams
- Set production reliability observability practices
Perks/Benefits
- Collaborative environment
- Fully remote option in Canada
- Mentoring and leadership opportunities
- Opportunity to shape organization wide machine learning and generative AI infrastructure strategy
Skills/Tech-stack
AWS | Access Control | Alerting | Audit Logging | Azure | Azure Machine Learning | CI/CD | Cloud platform | Compliance Monitoring | Databricks | Distributed Systems | Docker | Experiment tracking | Feature Store | Google Cloud | Google Cloud Platform | Java | Kubeflow | Kubernetes | LLM serving | MLOps | MLflow | Machine Learning | Model CI/CD | Model Registry | Monitoring | Multi-Factor | Multi-Factor Authentication | Network Security | Observability | Python | Ray | Role-Based Access | Role-Based Access Control | Serverless
Education
Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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