AI & Machine Learning Engineer, Level 17

• Design and maintain reusable ML assets, including feature pipelines, shared components, deployment patterns, and evaluation frameworks
• Collaborate with data scientists, architects, platform, and security teams to transition models from research to scalable, reliable production services
• Engineer and deploy production-grade ML and GenAI solutions using batch, real-time, and event-driven inference patterns
• Own or support the AI system lifecycle, including MLOps, LLMOps, Agent Ops, versioning, monitoring, retraining, scaling, rollback, and retirement
• Operationalize RAG-based and agentic GenAI applications with evaluation, guardrails, and cost awareness
• Embed security, governance, Responsible AI controls, Protected B requirements, auditability, and risk-based controls
• Automate AI delivery through CI/CD pipelines, Infrastructure as Code, and standardized environment promotion
• Monitor, diagnose, and remediate system health, model and data drift, bias indicators, cost anomalies, and production incidents within SLAs
• Build and validate predictive, descriptive, behavioural, and structured-data machine learning models
• Partner with business stakeholders and SMEs to translate insights into actionable recommendations
• Apply engineering judgment to balance performance, scalability, cost, security, and risk
• Provide fault isolation, initial resolution, concepts, and prototypes for AI product and service ideas

• University degree in Computer Science, Engineering, Mathematics, Data Science, or a related technical discipline
• Level 17:
  Minimum 5 years of experience in AI and ML engineering roles delivering production systems in enterprise environments
• Level 17:
  Minimum 5 years of experience designing or contributing to large-scale data platforms supporting batch and real-time workloads, primarily using Databricks and Azure
• Level 17:
  Minimum 3 years of hands-on experience building and operating ML or AI systems in production, including monitoring, retraining, and incident response
• Level 17:
  Minimum 3 years of experience with Azure cloud deployment, automation, networking, and security services, with focus on Databricks data operations
• Level 17:
  Minimum 3 years of experience implementing CI/CD pipelines and Infrastructure as Code for ML/AI workloads
• Level 17:
  Minimum 3 years of experience developing production-grade code using Python and data-centric languages such as SQL, Java, or Scala
• Level 17:
  Minimum 3 years of experience in formal IT service management and Agile delivery environments
• Level 17:
  Minimum 1 year of applied GenAI or MLOps experience, including LLMs, RAG-based architectures, or agentic/workflow-oriented patterns in production
• Level 18:
  Minimum 7 years of experience in AI and ML engineering roles delivering production systems in enterprise environments
• Level 18:
  Minimum 7 years of experience designing or contributing to large-scale data platforms supporting batch and real-time workloads, primarily using Databricks and Azure
• Level 18:
  Minimum 7 years of hands-on experience building and operating ML or AI systems in production
• Level 18:
  Minimum 7 years of experience with Azure cloud deployment, automation, networking, and security services
• Level 18:
  Minimum 7 years of experience implementing CI/CD pipelines and Infrastructure as Code for ML/AI workloads
• Level 18:
  Minimum 5 years of experience developing production-grade code using Python and data-centric languages
• Level 18:
  Minimum 7 years of experience in formal IT service management and Agile delivery environments
• Level 18:
  Minimum 3 years of applied GenAI or MLOps experience in production
• Candidates must be able to work legally in Canada at the time of application
• Candidates must meet government security screening requirements

Demonstrates…