AI ModelOps Engineer – Canadian Tire – Toronto, ON

Canadian Tire Corporation is looking for an AI ModelOps Engineer to join their Enterprise AI Platforms & AI ModelOps team in Toronto, Ontario. This is a technically rich role at the intersection of machine learning operations, cloud infrastructure, and the cutting edge of generative and agentic AI — a genuine opportunity to shape how AI is built, deployed, and governed at enterprise scale.

You’ll be working within CTC’s AI and Data group, contributing directly to the evolution of their AAAI platform and MLOps ecosystem. Day to day, you’ll design and operate the platforms that allow AI teams to build and deploy real solutions — from machine learning model lifecycle management to large language model (LLM) infrastructure, agentic workflows, and responsible AI practices.

About the Role: AI ModelOps Engineer

This role is about much more than keeping the lights on. As an AI ModelOps Engineer at Canadian Tire, you’ll help build and mature the foundational infrastructure that powers AI across a national enterprise. You’ll work hands-on with CI/CD pipelines, observability frameworks, model registries, and cloud-native tools on Microsoft Azure to ensure AI solutions are deployed reliably, securely, and at scale.

Collaboration is central to the work. You’ll partner with data scientists, AI engineers, cloud architects, software developers, and IT teams to operationalize AI solutions and raise the bar on engineering best practices. Whether it’s supporting governance and compliance, driving automation, or evaluating emerging AI tools and frameworks, you’ll be a key contributor to CTC’s enterprise AI strategy.

Benefits and Salary

The typical hiring range for this position is $80,000 to $131,000 per year, with compensation determined based on experience, skills, and market conditions. Canadian Tire offers a comprehensive benefits package including retirement programs, performance incentives, and continuing education support. Employees also receive mental health benefits of $5,000 per year (for eligible employees and their families), product discounts, Canadian Tire Profit Sharing, and access to career growth opportunities through the Triangle Learning Academy.

Responsibilities

In this role, you’ll own the end-to-end operational lifecycle of AI platforms — from design and deployment through monitoring, governance, and continuous improvement. Your work will directly enable CTC’s data scientists and AI engineers to build and ship solutions faster, more reliably, and with proper oversight.

• Design and implement platforms, tools, and processes supporting the full lifecycle of AI solutions across the enterprise
• Enable deployment and monitoring of machine learning models, generative AI solutions, and AI agents in production environments
• Contribute to the evolution of CTC’s AAAI platform and MLOps ecosystem, ensuring scalability, reliability, and security
• Establish and mature Agentic AI platform capabilities including tooling, registries, observability, and evaluation frameworks
• Develop and maintain automation and CI/CD pipelines that accelerate delivery, validation, and deployment of AI solutions
• Design observability solutions including monitoring, alerting, and troubleshooting for AI platform health and performance
• Collaborate cross-functionally with data scientists, cloud engineers, architects, and IT teams to operationalize AI solutions
• Evaluate and implement emerging AI platform technologies, frameworks, and engineering practices
• Support governance initiatives including model and agent lifecycle management, auditability, compliance, and responsible AI practices
• Partner with enterprise stakeholders to drive adoption and effective use of AI platforms and services across CTC

Requirements / Skills

The ideal candidate brings hands-on MLOps or AI platform engineering experience combined with a solid understanding of cloud infrastructure and machine learning operations. CTC values engineers who are curious, adaptable, and comfortable working across both technical depth and cross-functional collaboration.

• MLOps / GenAI operations experience including model deployment, monitoring, observability, automation, and lifecycle management
• Cloud platform proficiency, preferably Microsoft Azure, with experience building and operating AI or data platforms
• Generative AI and LLM knowledge, including RAG, AI agents, and emerging AI engineering practices
• Python proficiency with experience developing and integrating AI-enabled applications and services
• Experience with AI platforms such as Azure AI Foundry, Azure Machine Learning, Databricks, or MLflow
• Containerization and cloud-native skills with Docker, Kubernetes, and related orchestration platforms
• DevOps and IaC experience including CI/CD pipelines, source control, and tools like Terraform or Bicep
• Strong communication skills with the ability to convey complex technical concepts to both technical and non-technical audiences
• Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field — or an equivalent combination of education, certifications, and experience

How to Apply

To apply, visit the official job posting using the link below. Make sure your resume is up to date and tailored to the role before submitting your application.

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