AI ModelOps Engineer
Canadian Tire Corporation, Limited15 days ago
Toronto, CanadaSenior
Responsibilities
- Design, implement, operate, and continuously improve enterprise platforms, tools, and processes supporting the full lifecycle of AI solutions.
- Enable deployment, monitoring, governance, and lifecycle management for machine learning models, generative AI solutions, and AI agents.
- Evolve and operate CTC’s AAAI platform, MLOps ecosystem, and Agentic AI platform capabilities.
- Develop automation, CI/CD pipelines, platform services, reusable components, templates, libraries, and engineering standards.
- Implement observability, monitoring, logging, alerting, troubleshooting, and performance-management capabilities for production AI workloads.
- Support model and agent registries, evaluation frameworks, integrations, auditability, compliance controls, and responsible AI practices.
- Ensure AI platform availability, scalability, resilience, security, and cost efficiency through capacity planning and continuous improvement.
- Collaborate with technical and business stakeholders, lead technical discussions and architecture reviews, and promote adoption of AI platform capabilities.
- Evaluate emerging AI platform technologies and conduct post-deployment analysis and operational reviews.
Requirements
- Hands-on experience with MLOps, generative AI operations, or AI platform engineering, including 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, reliability, scalability, resiliency, troubleshooting, and root cause analysis.
- Experience with generative AI, large language models, retrieval-augmented generation, AI agents, and emerging AI engineering practices.
- Proficiency in Python and experience developing, integrating, and supporting AI-enabled applications and services.
- Experience with Azure AI Foundry, Azure Machine Learning, Databricks, MLflow, or comparable AI development and operational platforms.
- Experience with monitoring, logging, alerting, observability, and performance management for production AI workloads.
- Experience with Docker, Kubernetes, and related container orchestration technologies.
- Familiarity with DevOps, platform engineering, source control, automation, CI/CD pipelines, and Infrastructure as Code practices.
- Practical experience with Terraform, Bicep, or equivalent Infrastructure as Code tools.
- Knowledge of cloud security, governance, access management, compliance, and responsible AI practices.
- Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related discipline, or an equivalent combination of education, certifications, and practical experience.
- Strong communication, collaboration, stakeholder management, influencing, technical leadership, adaptability, and continuous-learning skills.
Benefits
- Comprehensive benefits and retirement programs.
- Performance incentives and continuing education programs.
- Additional well-being perks, career growth opportunities, and product discounts.
- Supported learning through the Triangle Learning Academy, profit sharing, and retirement and savings programs for eligible employees.
- Enhanced flex benefits include mental health benefits and mental health tools and resources for employees and eligible families.