Machine Learning Engineer Lead

Our Story &

Purpose:

We're Vancity, a member-owned credit union built on the principles of inclusion and social justice. Since 1946, our relentless commitment to these values has helped us challenge the status quo and break down barriers. We've made bold commitments to become net-zero by 2040 across all mortgages and loans, and we're actively pursuing strategies in Indigenous banking and financial resilience for our members.

As the largest private sector Living Wage Employer in Canada, we're proud to be consistently recognized as one of the country's Top Employers.

As a Machine Learning Engineer Lead
, you will join our Data Science & AI Pod, focused on designing, building, and deploying enterprise-wide AI and machine learning solutions that support business decision‑making, automation, and operational efficiency. This role is highly hands‑on and best suited for an engineer who can design, build, deploy, and operationalize machine learning models in enterprise production environments. You will work across the full ML lifecycle, including model development, feature engineering, MLOps, deployment automation, monitoring, and continuous improvement of machine learning systems.

Success in this role is measured by scalable, reliable, and production‑ready machine learning solutions—not proof‑of‑concepts or experimentation alone.

This is a Full-time, Permanent role and will report directly to the Manager, Data Science & AI. This position is remote and open to candidates located in British Columbia or Ontario. While this position provides a remote work arrangement, you will be expected to be on‑site for events and business demands.

• Applying Data Science and Machine Learning best practices to develop robust models and support data‑driven decision‑making across business domains
• Applying machine learning and data science techniques such as forecasting, predictive modeling, classification, regression, recommendation, and optimization to solve business problems
• Conducting experiments and evaluating models using appropriate statistical, technical, and business performance metrics
• Architecting, building, deploying, and maintaining scalable machine learning models and AI solutions integrated into enterprise systems, applications, and operational workflows
• Designing and implementing end‑to‑end ML workflows, including data preparation, feature engineering, model training, validation, deployment, optimization, and continuous monitoring in a high‑scale production environment
• Developing reusable machine learning components, feature pipelines, and model‑serving frameworks to support multiple use cases and teams
• Designing and implementing production‑grade MLOps solutions using Azure ML, Databricks, MLflow, and related cloud technologies