Senior Machine Learning Engineer

Senior Machine Learning Engineer Salary: $123,833.00 – $170,184.00 Location: Toronto, ON (Hybrid) Job Type & Duration: Full-time, 1 Permanent Vacancy Shift Information: Monday to Friday, 35 hours per week About the Role: Our client 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 their 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. Responsibilities Include: Design, test and implement scalable data and ML infrastructure components within the 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 What 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.

Design and operate scalable ML infrastructure and deployment frameworks on an AWS-based Enterprise Data Platform. Lead a team of engineers to integrate ML pipelines, ensure governance, and implement MLOps capabilities like model versioning and monitoring.

Requires a post-secondary degree in Computer Science or a related field with extensive experience in ML and data engineering within complex enterprise cloud environments. Proficiency in Python, AWS ML services, and MLOps practices is essential.

• CI/CDPreferred
• Amazon Web ServicesPreferred
• PythonPreferred

Identified from the job description. Confirm important requirements above.

• Machine Learning Engineering
• Data Engineering
• AWS
• MLOps
• Python
• TensorFlow
• PyTorch
• Scikit-learn
• AWS Bedrock
• AWS Sagemaker AI
• CI/CD
• Feature Store Design
• Model Governance
• Infrastructure Design
• API Integration
• Data Lakehouse Architecture

• Data & Analytics
• Technology
• Software
• Engineering
• Management & Leadership

Minimum education

Bachelor’s degree

Minimum experience

5+ years

Apply by

Sep 3, 2026

Posting language

English

Working hours

35 hours per week

Seniority

Mid-Senior level