Senior Machine Learning Engineer
- Toronto, ON, Canada
Contract
Applications open in a new tab.
Job details
- Job type
- Contract
- Apply by
- Posting language
- English
- Posted
Original job description
Job Title: Machine Learning Engineer – Senior
Location: Toronto, ON (Hybrid – 3 Days Onsite)
Client: Public Sector
Duration: 6+ Months Contract (Possibility of Extension)
Required Experience and Skills
- Extensive hands-on experience with Azure Machine Learning, Microsoft Foundry/Azure AI Foundry, and MLOps practices.
- Strong proficiency in Python and SQL, with demonstrated experience developing, testing, and deploying machine learning models into production environments.
- Experience implementing CI/CD pipelines, containers, APIs, automation, model deployment, monitoring, and observability.
- Strong understanding of machine learning algorithms, including classification, regression, clustering, feature engineering, and model evaluation.
- Experience with hyperparameter tuning, model optimization, performance troubleshooting, drift detection, and automated model retraining.
- Hands-on experience with Azure ML Studio, Azure AI Foundry, Databricks, Git, VS Code, Jira, Power BI, and Power Automate.
- Experience designing, developing, integrating, and operationalizing machine learning and deep learning solutions.
- Strong knowledge of statistics, data preprocessing, data cleaning, feature engineering, and validation strategies.
- Ability to analyze and visualize datasets, identify data distribution differences, and ensure data quality for reliable model performance.
- Experience defining business objectives, developing appropriate ML solutions, and establishing performance metrics to measure outcomes.
- Strong problem-solving, analytical, communication, and stakeholder management skills.
- Ability to manage technical resources, competing priorities, and project deadlines while collaborating with cross-functional teams.
Nice-to-Have Skills and Experience
- Previous public-sector or government experience.
- Experience establishing reusable MLOps frameworks, deployment patterns, and enterprise-wide ML automation.
- Experience with large-scale data processing using Azure Databricks and related cloud data platforms.
- Experience with production model monitoring, observability, drift detection, and continuous improvement.
- Familiarity with Power BI and Power Automate for analytics, reporting, and workflow automation.
- Experience integrating ML solutions with enterprise applications through APIs and automated workflows.
If this aligns with your experience, please share your updated resume with me at Afzal.Khan@buzzclan.com.
More about this job
Responsibilities
Design, develop, integrate, deploy, and operationalize machine learning and deep learning solutions in production, including CI/CD pipelines, automation, monitoring, and observability. Define business objectives and performance metrics, optimize and troubleshoot models, detect drift, and collaborate with stakeholders and cross-functional teams.
Requirements
Requires extensive hands-on experience with Azure Machine Learning, Azure AI Foundry, MLOps, Python, SQL, and production model development and deployment. Candidates should also have strong knowledge of machine learning methods, statistics, data preparation and validation, model optimization and monitoring, and the ability to manage priorities and communicate effectively.
Skills
- Azure Machine Learning
- Azure AI Foundry
- MLOps
- Python
- SQL
- Machine Learning
- Deep Learning
- CI/CD
- Containers
- APIs
- Model Deployment
- Model Monitoring
- Azure Databricks
- Feature Engineering
- Power BI
Visa sponsorship
Not detected in the job text
Hiring contact
Afzal Khan
Hiring contact email
Afzal.Khan@buzzclan.com
Categories
- Technology
- Data & Analytics
- Software
- Government & Public Sector
Keywords
- Azure Machine Learning
- Microsoft Foundry
- Azure AI Foundry
- MLOps
- Python
- SQL
- CI/CD Pipelines
- Containers
- APIs
- Machine Learning Algorithms
- Classification
- Regression
- Clustering
- Feature Engineering
- Model Evaluation
- Hyperparameter Tuning
- Model Optimization
- Model Deployment
- Model Monitoring
- Observability
- Drift Detection
- Automated Model Retraining
- Azure ML Studio
- Databricks
- Git
- VS Code
- Jira
- Power BI
- Power Automate
- Data Preprocessing