Rq00245 - Machine Learning Engineer - Senior

Client: Metrolinx – RQ00245
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
Must Have
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
DescriptionSenior 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.
General Skills
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.
Additional Information
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.
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