Senior machine learning scientist leading predictive AI teams at TD, a major Canadian bank. Developing regression, classification, and anomaly detection models for personal banking.
About the role
- Cloud Full Stack MLOps Engineer developing cloud applications and MLOps at Ontario Brain Institute. Collaborating with stakeholders to enhance workflows and support data models in production.
Responsibilities
- Design, develop, enhance, and maintain CfA cloud-based web applications software and supporting services.
- Support the productization of CfA applications and services for third-party use, including deployment and operational readiness.
- Implement and support MLOps capabilities, including data pipelines, model packaging, deployment, monitoring, and versioning.
- Support federated learning (FL) data and model deployment workflows and associated web applications, as applicable.
- Provide timely technical support to application users; triage issues, resolve incidents, and coordinate fixes with the development team.
- Develop and maintain technical documentation, including architecture, runbooks, and user guidance.
- Participate in application security audits, access reviews, and incident-response drills; implement remediation actions as needed.
- Work closely with other cloud team members to ensure secure and robust cloud resources are configured and managed with high standards.
Requirements
- Bachelor’s degree in computer science, software engineering, data science, or a related field, or an equivalent combination of education and experience.
- 5+ years of professional experience in software engineering, including delivery of production web applications and services.
- Demonstrated experience developing modern web applications (Python; React and/or Vue.js preferred).
- Proficiency with front-end user interface development and back-end services, including database integration.
- Hands-on experience deploying and operating ML models in production, including versioning and containerization.
- Experience developing data ingestion and processing pipelines (ETL/ELT), including custom data loaders.
- Experience working in cloud environments with CPU/GPU and distributed/grid computing (Microsoft Azure preferred).
- Experience delivering software in an Agile/Scrum environment, with strong collaboration and communication skills.
- Demonstrated experience supporting cloud MLOps deployments and operations for model lifecycle management (e.g., CI/CD for ML, model registries, experiment tracking, and monitoring).
- Experience applying engineering best practices, including source control, code review, automated testing, and CI/CD (GitHub experience preferred).
- Experience with Azure services (e.g., compute, storage, networking, identity) and deploying production workloads to Azure.
- Familiarity with security, privacy, and compliance practices for cloud applications (e.g., secure SDLC, threat modeling, vulnerability management).
- Experience supporting regulated or research environments requiring auditability and traceability.
Benefits
- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development opportunities
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Applied Machine Learning Scientist developing Generative AI and predictive ML solutions at TD, a major North American bank. Supporting model evaluation, deployment, monitoring, and responsible AI governance.
Applied Machine Learning Scientist developing Generative AI and predictive ML solutions for TD banking. Evaluating models, managing AI lifecycles, and supporting responsible implementation.