Senior Machine Learning Engineer

Senior Machine Learning Engineer TSD
Salary: $ – $
Division: Technology Services
Job Type & Duration: Full-time, 1 Permanent Vacancy
Shift Information: Monday to Friday, 35 hours per week

The City of Toronto is expanding its AWS based Enterprise Data Platform is seeking a Senior Machine Learning Engineer to design, build, and operate the data and machine learning infrastructure that enables AI at scale on the City’s AWS based Enterprise Data Platform. This role is not focused on standalone model experimentation. It is centered on building the engineering foundations, deployment frameworks, and ML capabilities required to support enterprise analytics and AI solutions.

The position sits at the intersection of data engineering, ML engineering, and platform enablement, ensuring that data and AI solutions are production ready, governed, and sustainable.

As a Senior Machine Learning Engineer, you will help establish the technical foundation that enables AI across City divisions. Your work will directly support the AWS infrastructure that powers forecasting, optimization, automation, and advanced analytics initiatives. You will join a growing Enterprise Data and AI team building modern capabilities on AWS. This is an opportunity to shape enterprise ML standards, define scalable MLOps practices, and design infrastructure that supports long term analytics and AI strategy across the organization.

• Design, test and implement scalable data and ML infrastructure components within the City’s AWS based Enterprise Data Platform
• Design feature engineering frameworks and support development of reusable feature stores aligned with enterprise data architecture
• Establish deployment patterns and operational standards for data and machine learning workloads across environments
• Ensure all ML platform components align with multi-account governance patterns and enterprise guardrails (account structure, logging/auditing, least privilege IAM, encryption, and centralized governance)
• Leads a team of data engineers and software engineers to integrate ML infrastructure into existing AWS Modern Data Architecture Accelerator (MDAA) standards. Motivating and training staff, ensuring effective teamwork, high standards of work quality and organizational performance, continuous learning and encourages innovation in others.
• Lead the design and ensure machine learning pipelines are secure, scalable, cost efficient, and aligned with governance and compliance requirements
• Develop and maintain architecture standards, templates, and reusable components for data and AI infrastructure
• Support integration of ML services into enterprise systems and APIs
• Implement monitoring and observability frameworks to detect data drift, model performance degradation, and operational issues
• Provides in-depth advice and makes recommendations to senior management and the Division regarding business solutions, enterprise architecture, infrastructure and operations, as well as enterprise transformation projects, to guide the adoption of machine learning and AI technologies for enhancing operational efficiency and service delivery across the organization.
• Build and operate foundational MLOps capabilities including model versioning, CI/CD integration, monitoring, and automated retraining workflows
• Contribute to enterprise AI governance practices including documentation, auditing, lifecycle management, and responsible AI controls
• Lead technical proof of concept initiatives to validate infrastructure patterns and scalability approaches
• Provide technical mentorship and guidance on ML engineering and MLOps best practices
• Present ML infrastructure roadmaps and architectural decisions to technical and business stakeholders