Machine Learning Engineer
CAN, Ontario, Toronto, 250 Bloor Street East, Canada
R
CAD 94K-144K Mid-level Full Time
Tasks
- Build feature and training pipelines
- Build reusable ML and GenAI patterns
- Design, train, evaluate, and deploy machine learning models
- Develop ML training inference and monitoring platforms
- Ensure responsible AI with governance and privacy controls
- Implement observability and alerting
- Integrate ML solutions with existing systems
- Integrate large language models for business problems
- Manage credentials and secrets
- Monitor model accuracy latency cost and drift
- Own ML CI CD backbone
- Provision infrastructure with Terraform
- Research and apply RAG vector search model fine tuning and orchestration
Perks/Benefits
- Dental insurance
- Disability insurance
- Employee assistance program
- Health insurance
- Life insurance
- Paid time off
- Retirement savings plan
- Vision insurance
- Wellness benefits
Skills/Tech-stack
AIOps | Alerting | Azure DevOps | Azure DevOps Boards | Azure Key Vault | Azure Logic | Azure Logic Apps | Azure Machine Learning | CI/CD | Data Governance | Data Pipelines | Databricks | Delta Lake | Delta Tables | DevOps Boards | Docker | ETL | Fine Tuning | Generative AI | Git | GitHub Actions | Infrastructure as Code | Java | JavaScript | Jenkins | Key Vault | Kubernetes | LLMOps | Langchain | Langgraph | Logic Apps | MLOps | MLflow | Machine Learning | Managed Identities | Model Fine-tuning | Model Governance | Monitoring | Observability | OpenAI SDK | Privacy controls | Python | RAG | Responsible AI | Retrieval-Augmented Generation | Scala | Spark | Terraform | Unity Catalog | Vector Search | “as-code”
Education
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