Director, Enterprise Machine Learning Frameworks & Operations
Toronto-81 Bay, 16th Floor, Canada
CAD 111K-152K (estimate) Executive-level Full Time
Tasks
- Architect and build reusable machine learning platform capabilities reference architectures and deployment patterns
- Define MLOps strategy and implementation tooling frameworks and engineering standards
- Develop automated ML pipelines for ingestion training validation and deployment
- Ensure regulatory and enterprise compliance with approval gates monitoring and audit ready processes
- Implement CI CD pipelines and end to end MLOps workflows
- Implement retrieval-augmented generation pipelines
- Lead design development and scaling of enterprise MLOps capabilities
- Maintain lineage and version tracking for models and data
- Mentor and upskill cross functional teams on MLOps tools and best practices
- Oversee model deployment pipeline management including APIs and open source models
- Partner with governance compliance and enterprise teams to meet standards early
- Set up monitoring frameworks for inference latency token usage and hallucination detection
Perks/Benefits
- Banking benefits
- Employee recognition program
- Hybrid work arrangement
- Incentive pay
- Paid day off for professional development
- Vacation offering
- Wellbeing support
Skills/Tech-stack
APIs | Audit Readiness | Azure ML | CI/CD | Databricks | Distributed Systems | Infrastructure as Code | Kubernetes | LLMOps | Language Models | Large Language Models | MLOps | Machine Learning | Model Governance | Model Risk Management | Model risk | Monitoring | Observability | Retrieval-Augmented Generation | Risk Management | “as-code”
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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