As an AI ModelOps Engineer, you will be part of the Enterprise AI Platforms & AI ModelOps team within the AI and Data group at Canadian Tire Corporation (CTC). In this role, you will support the infrastructure and provisioning of environments that enable AI teams to build and deploy agentic AI solutions. You will help design, implement, operate, and optimize the platforms, tools, and processes that support AI solutions across their lifecycle.

You will contribute to the evolution of CTC's AAAI platform and MLOps ecosystem, enabling the development, deployment, monitoring, and governance of machine learning solutions at scale. You will also help establish and mature CTC's Agentic AI platform capabilities, including support for generative AI, large language models (LLMs), AI agents, agentic workflows, and emerging AI engineering practices.

Responsibilities

Design, implement, operate, and continuously improve platforms, tools, and processes supporting the end-to-end lifecycle of AI solutions

Enable deployment, monitoring, governance, and lifecycle management of ML models, generative AI solutions, and AI agents in production

Contribute to the evolution and operational excellence of CTC's AAAI platform and MLOps ecosystem

Help establish and mature Agentic AI platform capabilities including infrastructure, tooling, registries, observability, and evaluation frameworks

Develop and maintain automation, CI/CD pipelines, and platform services that accelerate delivery of AI solutions

Design and implement observability, monitoring, alerting, and troubleshooting capabilities for AI platforms

Collaborate with data scientists, AI engineers, cloud engineers, architects, and IT teams

Evaluate, recommend, and implement emerging AI platform technologies and engineering practices

Support platform governance including model and agent lifecycle management,



auditability, and responsible AI practices

Contribute to platform standards, reusable components, reference architectures, and best practices

Ensure availability, scalability, resilience, security, and cost efficiency of AI platform infrastructure

Perform post-deployment analysis and operational reviews

Stay current with industry trends in ML, generative AI, Agentic AI, and cloud platforms

Requirements

Hands-on experience with MLOps, GenAI operations, or AI platform engineering

Experience building and operating AI, ML, or data platforms in cloud environments, preferably Microsoft Azure

Solid understanding of machine learning concepts, model lifecycle management, and governance

Experience with generative AI, LLMs, RAG, AI agents, and emerging AI engineering practices

Proficiency in Python and experience developing AI-enabled applications

Experience with Azure AI Foundry, Azure Machine Learning, Databricks, MLflow, or similar platforms

Experience with containerization: Docker, Kubernetes, and related orchestration platforms

Familiarity with DevOps practices including CI/CD pipelines, source control, automation, and IaC

Practical experience with Terraform, Bicep, or equivalent IaC tools

Knowledge of cloud security, governance, access management, and responsible AI practices

Excellent verbal and written communication skills

Bachelor's degree in Computer Science, Engineering, Information Technology, or related discipline

Bachelor's degree in Computer Science, Engineering, Information Technology, or related discipline, or equivalent combination of education, certifications, and experience. Hands-on MLOps or AI platform engineering experience. Proficiency in Python. Experience with Azure, Docker, Kubernetes, Terraform or Bicep, and AI platforms such as Azure AI Foundry or Databricks.

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📌 AI ModelOps Engineer (Ontario)
🏢 Canadian Tire Corporation
📍 Ontario