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Added 14 mins ago
SM
RQ00245 – Machine Learning Engineer – Senior
S M Software Solutions Inc
Toronto, ON
- Full-time
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…
Added 14 mins ago
SM
RQ11843 – Software Developer – Senior
S M Software Solutions Inc
Toronto, ON
- Internship
We are seeking a highly experienced Senior Software Developer with strong expertise in developing, designing, implementing, and maintaining n-tier enterprise applications. The ideal…
Added 14 mins ago
SM
RQ11857 – Systems Administrator/Operations Support Specialist – Senior
S M Software Solutions Inc
Toronto, ON
- Internship
We are seeking a highly experienced Senior Systems Administrator / Operations Support Specialist with strong expertise in Oracle Fusion Recruiting Cloud (ORC), Oracle HCM Cloud…
Added 45 mins ago
BG
Senior Cost Consultant (Estimator/Quantity Surveyor)
BTY Group
Toronto, ON
- $80,000 to $120,000 CAD / year
- Full-time
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Added 1 hour ago
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Lead, WELLTrust Consent & Patient Experience Program
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S M Software Solutions Inc

RQ00245 – Machine Learning Engineer – Senior

Toronto, ON, Canada

Hybrid

Full-time

Apply About employer

Applications open in a new tab.

Job details

Job type: Full-time
Workplace: Hybrid
Apply by: Dec 7, 2026
Posting language: English
Posted: Nov 7, 2026

Original job description

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

Description

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.

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.

How to Apply

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)

Please note that we cannot proceed with your submission without the following mandatory documents.

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You have received this message either as a result of contacting our company to express interest in employment or as a result of posting your resume in a location that can be accessed by recruiters or HR specialists, which suggests an interest in being contacted about employment opportunities.

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More about this job

Responsibilities

Deploy, integrate, monitor, and operationalize machine-learning and AI solutions using modern MLOps practices, creating reusable deployment patterns, tools, monitoring, and automation. Develop and evaluate models, run experiments, and help move solutions reliably from development 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 preparing data, engineering features, evaluating models, and troubleshooting production systems.

Skills

Python
Azure Machine Learning
Azure AI Foundry
MLOps
CI/CD
Containerization
APIs
Model Deployment
Model Monitoring
Observability
Machine Learning
SQL
Power BI
Power Automate
Databricks
Feature Engineering

Visa sponsorship

Not detected in the job text

Keywords

Python
SQL
Power BI
Power Automate
Visual Studio Code
Git
Jira
Azure Machine Learning
Azure AI Foundry
Databricks
MLOps
CI/CD
Containers
APIs
Automation
Model Deployment
Model Monitoring
Observability
Machine Learning
Deep Learning
Classification
Regression
Clustering
Feature Engineering
Model Evaluation
Hyperparameter Tuning
Model Drift
Model Retraining
Data Cleaning
Data Visualization

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