AI ModelOps Engineer

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 our 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. In parallel, you will 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. Through automation, observability, governance, and platform engineering, you will help accelerate the secure and responsible adoption of AI technologies across the enterprise. You will play a key role in operating, enhancing, and scaling the platforms and services that underpin CTC’s AI ecosystem. This includes enabling the reliable deployment, monitoring, governance, and lifecycle management of machine learning models, generative AI solutions, and AI agents in production environments. You will evaluate, implement, and support modern AI engineering capabilities, including observability, automation, model and agent registries, evaluation frameworks, and platform integrations that accelerate the adoption of enterprise AI solutions. Working closely with data scientists, AI engineers, cloud engineers, architects, and IT teams, you will ensure the stability, reliability, security, and performance of AI platforms while driving operational excellence through automation, continuous improvement, and platform innovation. What you'll do • Design, implement, operate, and continuously improve the platforms, tools, and processes that support the end-to-end lifecycle of AI solutions across the enterprise. • Enable the deployment, monitoring, governance, and lifecycle management of machine learning models, generative AI solutions, and AI agents in production environments. • Contribute to the evolution and operational excellence of CTC's AAAI platform and MLOps ecosystem, ensuring scalability, reliability, security, and performance. • Help establish and mature Agentic AI platform capabilities, including infrastructure, platform services, tooling, integrations, registries, observability, evaluation frameworks, and operational processes. • Develop and maintain automation, CI/CD pipelines, and platform services that accelerate the delivery, validation, deployment, and operation of AI solutions. • Design and implement observability, monitoring, alerting, and troubleshooting capabilities to ensure the health, reliability, and performance of AI platforms and workloads. • Collaborate with data scientists, AI engineers, cloud engineers, architects, software developers, and IT teams to operationalize AI solutions and promote engineering best practices. • Evaluate, recommend, and implement emerging AI platform technologies, tools, frameworks, and engineering practices that enhance platform capabilities and accelerate business value. • Support platform governance initiatives, including model and agent lifecycle management, auditability, operational controls, compliance requirements, and responsible AI practices. • Contribute to the development and adoption of platform standards, reusable components, reference architectures, templates, libraries, and best practices for AI engineering and operations. • Ensure the availability, scalability, resilience, security, and cost efficiency of AI platform infrastructure through proactive capacity planning, operational readiness, and continuous improvement initiatives. • Perform post-deployment analysis and operational reviews to identify optimization opportunities and improve platform reliability, efficiency, and user experience. • Partner with enterprise stakeholders to drive the adoption and effective use of AI platforms, services, and capabilities across CTC. • Stay current with industry trends and advancements in machine learning, generative AI, Agentic AI, cloud platforms, and AI engineering, providing recommendations for continuous platform evolution. What you bring • Hands-on experience with MLOps, GenAI operations, or AI platform engineering, including model deployment, monitoring, observability, automation, and lifecycle management. • Experience building and operating AI, machine learning, or data platforms in cloud environments, preferably Microsoft Azure. • Strong understanding of machine learning concepts, model lifecycle management, model governance, and operational best practices. • Experience with generative AI technologies, large language models (LLMs), retrieval-augmented generation (RAG), AI agents, and emerging AI engineering practices. • Proficiency in Python and experience developing, integrating, and supporting AI-enabled applications and services. • Experience with AI development and operational platforms such as Azure AI Foundry, Azure Machine Learning, Databricks, MLflow, or similar technologies. • Experience implementing monitoring, logging, alerting, observability, and performance management solutions for production AI workloads. • Experience with containerization and cloud-native technologies, such as Docker, Kubernetes, and related orchestration platforms. • Familiarity with DevOps and platform engineering practices, including CI/CD pipelines, source control, automation, and Infrastructure as Code (IaC). • Practical experience with Infrastructure as Code tools such as Terraform, Bicep, or equivalent technologies. • Knowledge of cloud security, governance, access management, compliance, and responsible AI practices. • Strong understanding of system reliability, scalability, resiliency, troubleshooting, and root cause analysis. • Excellent verbal and written communication skills, with the ability to collaborate effectively across technical and business teams. • Ability to lead technical discussions, architecture reviews, workshops, demonstrations, proofs of concept, and platform enablement activities. • Ability to communicate complex technical concepts to both technical and non-technical stakeholders. • Strong relationship-building, stakeholder management, and influencing skills. • Demonstrated curiosity, adaptability, and commitment to continuous learning in a rapidly evolving AI landscape. • Ability to work independently and collaboratively in a fast-paced, cross-functional environment. • Bachelor's degree in Computer Science, Engineering, Information Technology, or a related discipline, or an equivalent combination of education, certifications, and practical experience. We’re always looking for great talent! In addition to competitive pay, we offer: • Comprehensive benefits and retirement programs • Performance incentives, Continuing Education Programs • Other perks to support your well-being • Career growth opportunities and product discounts Our typical hiring range is between $80,000.00 and $131,000.00 per annum. Salary decisions are also dependent on other factors such as your experience, job-related knowledge, skills and competencies, market location, industry benchmarks, internal equity and other role-specific requirements. We're committed to attracting top talent. For critical roles, the compensation offering will be reviewed to ensure alignment with market rate and conditions and the unique value you bring to the role. #LI-AG2 This posting represents an existing vacancy within our organization. We may use artificial intelligence tools as part of our recruitment process to assist in the initial screening of resumes. All hiring decisions,