We are seeking an experienced Senior Machine Learning Engineer to deploy, integrate, monitor, and operationalize enterprise AI and machine learning solutions using modern MLOps practices. In this role, you will establish reusable deployment patterns, automated pipelines, monitoring frameworks, and infrastructure to reliably transition models fro
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m experimentation into scalable production environments. Leveraging deep expertise in Python, machine learning algorithms, statistical modeling, containerization, and the Azure AI and Databricks ecosystems, you will design and implement robust ML systems, detect data drift, automate retraining, and ensure high model reliability and performance across the enterprise.
Location: Toronto, ON (Hybrid – 3 days a week onsite)
Duration: 6-month contract.
Advantages
Modern MLOps & AI Stack: Work directly with Azure AI Foundry, Azure ML, Databricks, and containerized deployment pipelines.
Production Engineering Impact: Drive the transition of models from exploratory experimentation to scalable, reliable production services.
Full-Lifecycle Ownership: Lead end-to-end delivery spanning data preprocessing, model training, automated deployment, and continuous monitoring.
Flexible Hybrid Model: Balance focused remote work with collaborative onsite teamwork in Toronto.
Responsibilities
Design, build, deploy, and operationalize production-grade machine learning and deep learning systems.
Establish and implement end-to-end MLOps frameworks, CI/CD automation pipelines, model registry standards, and containerized deployment workflows.
Build, train, evaluate, and fine-tune machine learning models using advanced statistical techniques, feature engineering, and hyperparameter optimization.
Analyze complex datasets to uncover relationships between inputs and outputs, verifying data quality and engineering robust preprocessing pipelines.
Implement model monitoring, observability, and logging systems to track model performance, latency, data drift, and concept drift in production.
Design and execute automated model retraining strategies and fallback mechanisms to resolve production anomalies and performance degradation.
Expose and consume machine learning models via secure, low-latency RESTful APIs and integrate them into enterprise business workflows.
Collaborate closely with data architects, data scientists, software developers, and business stakeholders to align ML capabilities with organizational objectives.
Explore and visualize data distributions to assess real-world generalization and define rigorous validation strategies.
Author comprehensive technical documentation, architecture blueprints, runbooks, and deployment guides to support long-term maintainability.
Qualifications
Advanced MLOps & Azure Ecosystem: Advanced hands-on experience with Azure Machine Learning, Microsoft Foundry / Azure AI Foundry, MLOps practices, CI/CD pipelines, containerization, RESTful APIs, automation, model deployment, monitoring, and observability (Strictly Required).
Production ML Engineering & Python: Strong Python and machine learning engineering skills with demonstrated experience taking models from experimentation into reliable, scalable production environments (Strictly Required).
Technical Stack Proficiency: Hands-on experience working across the core technical ecosystem: Python, SQL, Power BI, Power Automate, VS Code, Git, Jira, Azure (ML Studio, AI Foundry), and Databricks (Strictly Required).
Algorithms, Statistics & Lifecycle Management: Deep understanding of ML algorithms and statistics, including classification, regression, clustering, feature engineering, model evaluation, hyperparameter tuning, data drift, retraining triggers, performance optimization, and production troubleshooting (Strictly Required).
Collaborative & Agile Delivery: Proven ability to manage competing priorities, meet tight deadlines, and work effectively with cross-functional technical squads and business teams (Strictly Required).
Nice-to-Have Assets:
Prior experience delivering machine learning or MLOps solutions within a public sector or large enterprise environment.
Relevant cloud or AI certifications (e.g., Azure Machine Learning Associate, Azure Data Scientist Associate).
Experience with automated testing of data and ML pipelines.
Summary
If you're interested in the "Senior Machine Learning Engineer" role based in Toronto, we encourage you to apply online at www.randstad.ca.
Only qualified candidates will be contacted for the next steps. We look forward to hearing from you!
Randstad Canada is committed to fostering a workforce reflective of all peoples of Canada. As a result, we are committed to developing and implementing strategies to increase the equity, diversity and inclusion within the workplace by examining our internal policies, practices, and systems throughout the entire lifecycle of our workforce, including its recruitment, retention and advancement for all employees. In addition to our deep commitment to respecting human rights, we are dedicated to positive actions to affect change to ensure everyone has full participation in the workforce free from any barriers, systemic or otherwise, especially equity-seeking groups who are usually underrepresented in Canada's workforce, including those who identify as women or non-binary/gender non-conforming; Indigenous or Aboriginal Peoples; persons with disabilities (visible or invisible) and; members of visible minorities, racialized groups and the LGBTQ2+ community.
Randstad Canada is committed to creating and maintaining an inclusive and accessible workplace for all its candidates and employees by supporting their accessibility and accommodation needs throughout the employment lifecycle. We ask that all job applications please identify any accommodation requirements by sending an email to accessibility@randstad.ca to ensure their ability to fully participate in the interview process.
This posting is for existing and upcoming vacancies.
show more
We are seeking an experienced Senior Machine Learning Engineer to deploy, integrate, monitor, and operationalize enterprise AI and machine learning solutions using modern MLOps practices. In this role, you will establish reusable deployment patterns, automated pipelines, monitoring frameworks, and infrastructure to reliably transition models from experimentation into scalable production environments. Leveraging deep expertise in Python, machine learning algorithms, statistical modeling, containerization, and the Azure AI and Databricks ecosystems, you will design and implement robust ML systems, detect data drift, automate retraining, and ensure high model reliability and performance across the enterprise.
Location: Toronto, ON (Hybrid – 3 days a week onsite)
Duration: 6-month contract.
Advantages
Modern MLOps & AI Stack: Work directly with Azure AI Foundry, Azure ML, Databricks, and containerized deployment pipelines.
Production Engineering Impact: Drive the transition of models from exploratory experimentation to scalable, reliable production services.
Full-Lifecycle Ownership: Lead end-to-end delivery spanning data preprocessing, model training, automated deployment, and continuous monitoring.
...
Flexible Hybrid Model: Balance focused remote work with collaborative onsite teamwork in Toronto.
Responsibilities
Design, build, deploy, and operationalize production-grade machine learning and deep learning systems.
Establish and implement end-to-end MLOps frameworks, CI/CD automation pipelines, model registry standards, and containerized deployment workflows.
Build, train, evaluate, and fine-tune machine learning models using advanced statistical techniques, feature engineering, and hyperparameter optimization.
Analyze complex datasets to uncover relationships between inputs and outputs, verifying data quality and engineering robust preprocessing pipelines.
Implement model monitoring, observability, and logging systems to track model performance, latency, data drift, and concept drift in production.
Design and execute automated model retraining strategies and fallback mechanisms to resolve production anomalies and performance degradation.
Expose and consume machine learning models via secure, low-latency RESTful APIs and integrate them into enterprise business workflows.
Collaborate closely with data architects, data scientists, software developers, and business stakeholders to align ML capabilities with organizational objectives.
Explore and visualize data distributions to assess real-world generalization and define rigorous validation strategies.
Author comprehensive technical documentation, architecture blueprints, runbooks, and deployment guides to support long-term maintainability.
Qualifications
Advanced MLOps & Azure Ecosystem: Advanced hands-on experience with Azure Machine Learning, Microsoft Foundry / Azure AI Foundry, MLOps practices, CI/CD pipelines, containerization, RESTful APIs, automation, model deployment, monitoring, and observability (Strictly Required).
Production ML Engineering & Python: Strong Python and machine learning engineering skills with demonstrated experience taking models from experimentation into reliable, scalable production environments (Strictly Required).
Technical Stack Proficiency: Hands-on experience working across the core technical ecosystem: Python, SQL, Power BI, Power Automate, VS Code, Git, Jira, Azure (ML Studio, AI Foundry), and Databricks (Strictly Required).
Algorithms, Statistics & Lifecycle Management: Deep understanding of ML algorithms and statistics, including classification, regression, clustering, feature engineering, model evaluation, hyperparameter tuning, data drift, retraining triggers, performance optimization, and production troubleshooting (Strictly Required).
Collaborative & Agile Delivery: Proven ability to manage competing priorities, meet tight deadlines, and work effectively with cross-functional technical squads and business teams (Strictly Required).
Nice-to-Have Assets:
Prior experience delivering machine learning or MLOps solutions within a public sector or large enterprise environment.
Relevant cloud or AI certifications (e.g., Azure Machine Learning Associate, Azure Data Scientist Associate).
Experience with automated testing of data and ML pipelines.
Summary
If you're interested in the "Senior Machine Learning Engineer" role based in Toronto, we encourage you to apply online at www.randstad.ca.
Only qualified candidates will be contacted for the next steps. We look forward to hearing from you!
Randstad Canada is committed to fostering a workforce reflective of all peoples of Canada. As a result, we are committed to developing and implementing strategies to increase the equity, diversity and inclusion within the workplace by examining our internal policies, practices, and systems throughout the entire lifecycle of our workforce, including its recruitment, retention and advancement for all employees. In addition to our deep commitment to respecting human rights, we are dedicated to positive actions to affect change to ensure everyone has full participation in the workforce free from any barriers, systemic or otherwise, especially equity-seeking groups who are usually underrepresented in Canada's workforce, including those who identify as women or non-binary/gender non-conforming; Indigenous or Aboriginal Peoples; persons with disabilities (visible or invisible) and; members of visible minorities, racialized groups and the LGBTQ2+ community.
Randstad Canada is committed to creating and maintaining an inclusive and accessible workplace for all its candidates and employees by supporting their accessibility and accommodation needs throughout the employment lifecycle. We ask that all job applications please identify any accommodation requirements by sending an email to accessibility@randstad.ca to ensure their ability to fully participate in the interview process.
This posting is for existing and upcoming vacancies.
show more
