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.

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 modelling, classification, regression, recommendation, and optimisation 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, optimisation, 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 re-training 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 re-training 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 productionising 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 modelling, feature engineering, and model evaluation techniques

Proven ability to take solutions from prototype to production

Extra Sk

📌 Machine Learning Engineer Lead (Vancouver)
🏢 Vancity (2.0)
📍 Vancouver