Machine Learning Engineer (FinTech)

Location: Canada; remote

Company

Data & AI – Machine Learning department focused on building and operating foundational machine learning models, platforms, and pipelines.

What you will do

• Build, train, deploy, and maintain machine learning models in production environments.
• Develop robust, scalable, and cost-efficient ML and AI pipelines and platforms.
• Establish engineering standards across coding, testing, MLOps, and automated workflows.
• Collaborate with risk specialists, product leads, and software developers to translate business needs into technical specifications and integrate ML features into applications.
• Implement model lineage, data protection, fairness, compliance, and governance controls.
• Build observability systems for monitoring model health, operational metrics, and business value.

Requirements

• 3–5 years of professional machine learning engineering experience, including production model deployment.
• Experience with modern data ingestion workflows and curated data warehouses such as Databricks or Redshift.
• At least 3 years of hands-on experience with AWS infrastructure, including SageMaker, Spark or AWS Glue, and Terraform.
• Strong proficiency with Airflow or similar orchestration systems for multi-stage training and deployment workflows.
• Practical experience with MLflow, Kubeflow, or SageMaker Feature Store.
• Knowledge of model governance, data lineage, fairness, privacy, data cataloging, and compliance practices, plus strong communication skills with non-technical stakeholders.

Nice to have

• Experience in FinTech or financial risk environments.

Culture & Benefits

• Remote work arrangement.
• Bonus structure.
• Employer-paid benefits plan.
• Health and wellness flex account.
• Wellness days, paid holiday shutdown, and additional summer vacation days.
• Google Gemini may be used during interviews for confidential note-taking only.