Senior Machine Learning Engineer TSD Salary:$123,833.00 – $170,184.00 Division:Technology Services Job Type & Duration:Full-time, 1 Permanent Vacancy Shift Information:Monday to Friday, 35 hours per weekAbout the Role 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.Why Join the City of Toronto 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.Responsibilities Include:Design, test and implement scalable data and ML infrastructure components within the City’s AWS based Enterprise Data PlatformDesign feature engineering frameworks and support development of reusable feature stores aligned with enterprise data architectureEstablish deployment patterns and operational standards for data and machine learning workloads across environmentsEnsure 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 requirementsDevelop and maintain architecture standards, templates, and reusable components for data and AI infrastructureSupport integration of ML services into enterprise systems and APIsImplement monitoring and observability frameworks to detect data drift, model performance degradation, and operational issuesProvides 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 workflowsContribute to enterprise AI governance practices including documentation, auditing, lifecycle management, and responsible AI controlsLead technical proof of concept initiatives to validate infrastructure patterns and scalability approachesProvide technical mentorship and guidance on ML engineering and MLOps best practicesPresent ML infrastructure roadmaps and architectural decisions to technical and business stakeholdersWhat do you bring to the role:Post-secondary education in Computer Science, Engineering, Data Science, or a related discipline, or an equivalent combination of education and experience.Extensive experience in Machine Learning engineering, data engineering, or AI platform within complex enterprise environments.Considerable experience designing, building and operationalizing data and Machine Learning (ML) infrastructure in cloud environments (e.g., standardized ML environments, reusable pipeline templates, deployment foundations and monitoring).Considerable experience in Python and working with ML frameworks (e.g., TensorFlow, PyTorch, or scikit learn).Experience with AWS ML services (e.g., Bedrock/Sagemaker AI) and related managed ML services from a platform and enablement perspective, rather than model research.Experience building ML infrastructure on AWS based data lakehouse architectures and integrating ML services into enterprise systems and APIs.Experience implementing MLOps practices including CI/CD for ML workloads, model versioning, and automated retraining.Experience working with large scale structured and unstructured datasets in cloud environments.Understanding of model governance, explainability, and responsible AI practices.Strong communication skills and ability to translate technical architecture into business value.Familiarity with feature store design and implementation, AWS DataZone or enterprise data governance frameworks are assets.Experience working in public sector or regulated environments is an asset.Equity, Diversity and Inclusion The City is an equal opportunity employer, dedicated to creating a workplace culture of inclusiveness that reflects the diverse residents that we serve. Learn more about theCity’s commitment to employment equity.Accommodation The City of Toronto is committed to creating an accessible and inclusive organization. We are committed to providing barrier-free and accessible employment practices in compliance with the AODA. Should you require Code-protected accommodation through any stage of the recruitment process, please make them known when contacted and we will work with you to meet your needs. Disability-related accommodation during the application process is available upon request. Learn more about the City’s Hiring Policies and Accommodation Process.#J-18808-Ljbffr