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. If you’re ready to join our team of 2,700 diverse individuals, access competitive rewards and benefits, and be part of a greater movement - apply today! Your Role in Supporting Our Members: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 How You'll Make an Impact:
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
Building and maintaining automated ML pipelines, feature engineering workflows, feature store patterns, and deployment processes for training, testing, monitoring, and retraining machine learning models
Implementing standards and best practices for model versioning, lifecycle management, governance, deployment automation, model performance monitoring, drift detection, data quality, operational health, and retraining triggers
Developing production-quality Python code, APIs, automation workflows, and machine learning services to integrate ML capabilities into business applications and processes
What You’ll Bring to the Team:
10+ years of experience in Machine Learning Engineering, Data Science, Applied AI, Software Engineering, or related disciplines
Bachelor’s or Master’s degree in Computer Science, Software Engineering, Mathematics, Statistics, or a related quantitative field
Strong hands-on experience building and deploying cloud-based applications and machine learning services
Strong proficiency in Python and SQL, with solid software engineering fundamentals including data structures, algorithms, and object-oriented design
Hands-on experience with industry-standard machine learning and deep learning frameworks such as PyTorch, TensorFlow, and Scikit-learn
Proven experience productionizing machine learning models and operating scalable, reliable ML systems in enterprise environments
Hands-on experience with Azure Machine Learning, Databricks, MLflow, CI/CD pipelines, model lifecycle management, monitoring, and deployment automation
Strong understanding of API design and service integration, machine learning algorithms, statistical modeling, feature engineering, and model evaluation techniques
Proven ability to take solutions from prototype to production
Extra Skills That Set You Apart:
Exposure to machine learning use cases such as churn prediction, forecasting, predictive modeling, member or customer personalization, recommendation systems, marketing optimization, and experimentation frameworks such as A/B testing
Familiarity with advanced machine learning techniques including anomaly detection, graph neural networks, optimization methods, representation learning, causal inference, and Generative AI workflows
Experience integrating AI services into automation platforms such as UiPath or Power Automate and familiarity with AWS, GCP, Power BI, or Tableau
You’ll Thrive Here If You Are:
Production-Focused - You measure success by scalable, reliable, and production-ready machine learning solutions
Hands-On - You enjoy designing, building, deploying, and operationalizing machine learning solutions from end to end
Collaborative - You work effectively with data, platform, cloud, security, and business teams to deliver meaningful outcomes
Technically Curious - You continuously explore emerging technologies, machine learning techniques, and AI innovations
Quality-Driven - You prioritize engineering excellence, automation, testing, monitoring, and maintainable code
We value