Job Description
As a Senior Machine Learning Engineer, you will be the bridge between data science theory and production-grade reality. You will design, develop, and deploy robust ML pipelines and services across a hybrid infrastructure. This role is deeply technical, requiring a "senior mindset" where you take ownership of model observability, registration, and the selection of the right algorithms for the right problems. You will work within a sophisticated stack (Databricks, Terraform, Snowflake, and AWS) to ensure that ML models don't just "work" in a notebook, but provide sustained value in a live, distributed environment.
...
Advantages
Advantages
Flexible Engagement: Primarily contract-based with a strong openness to Contract-to-Hire or direct Permanent Full-Time for the right fit.
Scale & Impact: Work on a high-visibility roadmap where your contributions directly affect project delivery timelines for a global brand.
Professional Growth: Access to outstanding career development, supported professional education, and mentorship from top-tier technical leads.
Culture of Belonging: Join an organization that values diversity and inclusion through employee-driven programs (LGBTQ+, gender, and origins) and robust wellness initiatives.
Work-Life Balance: A standard 37.5-hour work week with a hybrid model (3 days onsite in Markham/Toronto/Oakville).
Responsibilities
Responsibilities
End-to-End Deployment: Design and maintain scalable ML workflows and services in both Cloud (AWS) and On-Premise environments.
MLOps Excellence: Implement best practices for CI/CD, model versioning, monitoring, and automated retraining using Jenkins and Docker.
Infrastructure as Code: Utilize Terraform and Databricks to manage environments and model lifecycles.
Optimization: Identify bottlenecks in "working" code; perform trade-off analysis between technical debt and speed of delivery to ensure high performance.
Cross-Functional Collaboration: Partner with Data Scientists and Product teams to translate business requirements into backend API developments and data pipelines.
Mentorship: Provide technical leadership and guidance to the broader engineering team, advocating for clean code and architectural integrity.
Qualifications
Qualifications & Certificates
Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
Experience: 5+ years of hands-on experience in ML Engineering or Backend Engineering with a heavy ML focus.
Technical Stack: * Languages: Expert-level Python (scikit-learn, XGBoost) and SQL (query optimization/tuning).
Cloud/Data: Deep experience with Snowflake (Snowpark), AWS (S3, EC2, ECR), and Databricks.
DevOps: Strong proficiency in Terraform, Jenkins, and Docker.
Systems: Comfort working in Linux-based systems via remote SSH.
The "Bar": You must be able to demonstrate an analytical mindset—specifically how you handle model drift, error handling in distributed pipelines, and model observability.
Asset: Familiarity with Feature Stores, MLflow, Airflow, or DVC.
Summary
Summary
This is not a role for those looking to "learn on the job." We need battle-tested engineers who can hit the ground running within a 2–3 week window. If you are an Ontario-based professional (commutable to Markham/Toronto) who excels at building production-ready ML systems and can articulate the logic behind your technical choices, we want to hear from you.
Note: Our screening process includes a rigorous technical deep-dive. Candidates will be asked to walk through their approach to optimization, trade-off evaluation, and ML failure prevention strategies.
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
Job Description
As a Senior Machine Learning Engineer, you will be the bridge between data science theory and production-grade reality. You will design, develop, and deploy robust ML pipelines and services across a hybrid infrastructure. This role is deeply technical, requiring a "senior mindset" where you take ownership of model observability, registration, and the selection of the right algorithms for the right problems. You will work within a sophisticated stack (Databricks, Terraform, Snowflake, and AWS) to ensure that ML models don't just "work" in a notebook, but provide sustained value in a live, distributed environment.
Advantages
Advantages
Flexible Engagement: Primarily contract-based with a strong openness to Contract-to-Hire or direct Permanent Full-Time for the right fit.
Scale & Impact: Work on a high-visibility roadmap where your contributions directly affect project delivery timelines for a global brand.
Professional Growth: Access to outstanding career development, supported professional education, and mentorship from top-tier technical leads.
...
Culture of Belonging: Join an organization that values diversity and inclusion through employee-driven programs (LGBTQ+, gender, and origins) and robust wellness initiatives.
Work-Life Balance: A standard 37.5-hour work week with a hybrid model (3 days onsite in Markham/Toronto/Oakville).
Responsibilities
Responsibilities
End-to-End Deployment: Design and maintain scalable ML workflows and services in both Cloud (AWS) and On-Premise environments.
MLOps Excellence: Implement best practices for CI/CD, model versioning, monitoring, and automated retraining using Jenkins and Docker.
Infrastructure as Code: Utilize Terraform and Databricks to manage environments and model lifecycles.
Optimization: Identify bottlenecks in "working" code; perform trade-off analysis between technical debt and speed of delivery to ensure high performance.
Cross-Functional Collaboration: Partner with Data Scientists and Product teams to translate business requirements into backend API developments and data pipelines.
Mentorship: Provide technical leadership and guidance to the broader engineering team, advocating for clean code and architectural integrity.
Qualifications
Qualifications & Certificates
Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
Experience: 5+ years of hands-on experience in ML Engineering or Backend Engineering with a heavy ML focus.
Technical Stack: * Languages: Expert-level Python (scikit-learn, XGBoost) and SQL (query optimization/tuning).
Cloud/Data: Deep experience with Snowflake (Snowpark), AWS (S3, EC2, ECR), and Databricks.
DevOps: Strong proficiency in Terraform, Jenkins, and Docker.
Systems: Comfort working in Linux-based systems via remote SSH.
The "Bar": You must be able to demonstrate an analytical mindset—specifically how you handle model drift, error handling in distributed pipelines, and model observability.
Asset: Familiarity with Feature Stores, MLflow, Airflow, or DVC.
Summary
Summary
This is not a role for those looking to "learn on the job." We need battle-tested engineers who can hit the ground running within a 2–3 week window. If you are an Ontario-based professional (commutable to Markham/Toronto) who excels at building production-ready ML systems and can articulate the logic behind your technical choices, we want to hear from you.
Note: Our screening process includes a rigorous technical deep-dive. Candidates will be asked to walk through their approach to optimization, trade-off evaluation, and ML failure prevention strategies.
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
