RQ00245 – Machine Learning Engineer – Senior
- Toronto, ON, Canada
Hybrid
Full-time
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Job details
- Job type
- Full-time
- Workplace
- Hybrid
- Apply by
- Posting language
- English
- Posted
Original job description
We are seeking a highly skilled Senior Machine Learning Engineer with advanced hands-on experience in Azure Machine Learning, Microsoft Foundry/Azure AI Foundry, Machine Learning Operations (MLOps), CI/CD, containers, APIs, automation, model deployment, monitoring, and observability. The ideal candidate will have strong Python and machine-learning engineering expertise, with a proven ability to take machine-learning models from experimentation through development and into reliable production environments. The successful candidate will design, develop, deploy, integrate, monitor, and operationalize AI and machine-learning solutions using modern MLOps practices, establishing reusable deployment patterns, tools, monitoring, and automation to support enterprise-scale AI solutions.
Role Snapshot
- Job Title: RQ00245 – Machine Learning Engineer – Senior
- Client: Metrolinx
- Work Location: 277 Front Street West, Toronto, Ontario, Canada
- Work Arrangement: Hybrid – 3 Days per Week Onsite
- Estimated Start Date: November 23, 2026
- Estimated End Date: May 23, 2027
- Business Days: 130.00
- Hours: 7.25 hours per day
- Public Sector Experience: Preferred but not required
- Advanced hands-on experience with Azure Machine Learning, Microsoft Foundry/Azure AI Foundry, MLOps, CI/CD, containers, APIs, automation, model deployment, monitoring, and observability.
- Strong Python and machine-learning engineering skills, with demonstrated experience taking models from experimentation into reliable production environments.
- Experience with the following technology stack:
- Python
- SQL
- Power BI
- Power Automate
- Visual Studio Code (VS Code)
- Git
- Jira
- Azure (ML Studio, AI Foundry)
- Databricks
- Deep understanding of machine-learning algorithms and statistics, including:
- Classification
- Regression
- Clustering
- Feature Engineering
- Model Evaluation
- Hyperparameter Tuning
- Model Drift
- Model Retraining
- Performance Optimization
- Production Model Troubleshooting
Senior Machine Learning Engineer
The Senior Machine Learning Engineer will deploy, integrate, monitor, and operationalize AI and machine-learning solutions using modern MLOps practices. The role will establish reusable deployment patterns, tooling, monitoring, and automation that enable AI solutions to move reliably from development into production and scale across the enterprise.
Responsibilities
- Deploy, integrate, monitor, and operationalize AI and machine-learning solutions using modern MLOps practices.
- Establish reusable deployment patterns, tooling, monitoring, and automation.
- Enable AI solutions to move reliably from development into production and scale across the enterprise.
- Create machine-learning models and utilize data to train models.
- Focus on analysing data to find relationships between the input and the desired output.
- Understand business objectives and develop models that help achieve them, along with metrics to track their progress.
- Design and develop machine-learning and deep-learning systems.
- Run machine-learning tests and experiments.
- Implement appropriate machine-learning algorithms.
- Experience managing available resources such as hardware, data, and personnel to ensure deadlines are met.
- Experience analysing machine-learning algorithms that could be used to solve a given problem and ranking them by their probability of success.
- Experience exploring and visualizing data to gain an understanding of it, and then identifying differences in data distribution that could affect performance when deploying the model in real-world environments.
- Experience verifying data quality and/or ensuring data quality through data cleaning.
- Experience supervising the data acquisition process when additional data is required.
- Experience finding available datasets online that could be used for training.
- Experience defining validation strategies.
- Experience defining the preprocessing or feature engineering to be performed on a given dataset.
- Background in statistics and computer programming.
- A team player with a track record of meeting deadlines, managing competing priorities, and demonstrating client relationship management experience.
- Work Location: 277 Front Street West, Toronto, Ontario, Canada.
- Work Arrangement: Hybrid.
- Onsite Requirement: 3 days per week onsite.
- Public Sector Experience: Preferred but not required.
- Contract Duration: November 23, 2026, to May 23, 2027.
- Working Hours: 7.25 hours per day.
If you are interested and your profile matches the requirements, kindly submit the following mandatory documents to hrsmss@smsoftconsulting.com by Monday, November 9, 2026, at 8:00 AM EST.
Mandatory Documents
- Updated Resume in Word format (Mandatory)
- Skills Matrix and References (Mandatory)
- Expected Hourly Rate (Mandatory)
- Visa Status (Mandatory)
- LinkedIn ID (Mandatory)
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More about this job
Responsibilities
Deploy, integrate, monitor, and operationalize machine-learning and AI solutions using MLOps practices, creating reusable deployment patterns, tooling, monitoring, and automation. Develop and test models, prepare and analyze data, select appropriate algorithms, and move solutions reliably from experimentation into production at enterprise scale.
Requirements
Requires advanced hands-on experience with Azure Machine Learning or Azure AI Foundry, MLOps, CI/CD, containers, APIs, automation, deployment, monitoring, and observability, along with strong Python and machine-learning engineering skills. Candidates should understand machine-learning algorithms and statistics and have experience with data preparation, feature engineering, validation, model evaluation, troubleshooting, and production operations.
Skills
- Azure Machine Learning
- Azure AI Foundry
- MLOps
- CI/CD
- Python
- Machine Learning
- Model Deployment
- Model Monitoring
- Observability
- Containerization
- APIs
- Automation
- SQL
- Databricks
- Power BI
- Power Automate
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Data & Analytics
- Engineering
Keywords
- Azure Machine Learning
- Azure AI Foundry
- Microsoft Foundry
- MLOps
- CI/CD
- Python
- SQL
- Power BI
- Power Automate
- Visual Studio Code
- Git
- Jira
- Databricks
- Machine Learning
- Deep Learning
- Classification
- Regression
- Clustering
- Feature Engineering
- Model Evaluation
- Hyperparameter Tuning
- Model Drift
- Model Retraining
- Model Deployment
- Model Monitoring
- Observability
- Containers
- APIs
- Data Quality
- Production Troubleshooting