Senior Machine Learning Engineer

• Design and maintain scalable ML workflows and services in both Cloud (AWS) and On-Premise environments
• Implement best practices for CI/CD, model versioning, monitoring, and automated retraining using Jenkins and Docker
• Utilize Terraform and Databricks to manage environments and model lifecycles
• Identify bottlenecks in 'working' code
• perform trade-off analysis between technical debt and speed of delivery to ensure high performance
• Partner with Data Scientists and Product teams to translate business requirements into backend API developments and data pipelines
• Provide technical leadership and guidance to the broader engineering team, advocating for clean code and architectural integrity

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field
• 5+ years of hands-on experience in ML Engineering or Backend Engineering with a heavy ML focus
• Expert-level Python (scikit-learn, XGBoost) and SQL (query optimization/tuning)
• Deep experience with Snowflake (Snowpark), AWS (S3, EC2, ECR), and Databricks
• Strong proficiency in Terraform, Jenkins, and Docker
• Comfort working in Linux-based systems via remote SSH
• Analytical mindset—specifically how you handle model drift, error handling in distributed pipelines, and model observability

Familiarity with Feature Stores, MLflow, Airflow, or DVC

• 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)