Senior AI Platform Operations Engineer

Main Activities

AI Platform Reliability and Operations
•    Administer and operate the AI platform to ensure availability, performance, and resilience across environments, integrations, and supporting infrastructure.
•    Monitor platform health using dashboards, logs, metrics, and alerts, and coordinate incident and service restoration activities.
•    Lead operational triage, escalation coordination, and post-incident reviews to strengthen services stability and resilience. 
•    Track and report on service reliability indicators, incident trends, and operational performance.

AI Platform Enablement & Production Readiness
•    Enable approved AI use cases into production by ensuring: 
o    Environment readiness, 
o    Dependency validation, 
o    Completion of operational readiness checklists, 
o    Structured service transition activities
•    Support platform lifecycle management through:
o    Release coordination, 
o    Change readiness validation, 
o    Maintenance and capacity planning.
•    Ensure AI platform changes meet defined operational and control readiness criteria prior to release

Observability, Automation & AI Ops
•    Implement and maintain observability capabilities, including telemetry, logging, metrics, and traces required for enterprise AI operations.
•    Analyze operational data to identify anomalies, recurring issues, root-cause patterns.
•    Implement AI Ops use cases such as:
o    Alert correlation, 
o    Anomaly detection, 
o    Root-cause support, 
o    Forecasting and predictive insights, 
o    Automation of repetitive operational tasks.
•    Continuously improve operational efficiency through targeted automation and process optimization.

Governance, Risk, & Control Execution
•    Execute governance controls for AI solutions, including:
o    Usage and access controls, 
o    Data privacy considerations, 
o    Auditability and traceability, 
o    Human oversight requirements
•    Ensure operational practices align with enterprise security policies, risk controls, and compliance requirements.
•    Maintain documentation and evidence required for audit, governance reviews, production readiness checkpoints, and control validation.
•    Identify control gaps and escalate risks appropriately to relevant governance and risk stakeholders.

AI Asset Visibility & Operational Integrity
•    Maintain operational visibility of AI platform assets required for monitoring, support, and cost alignment. 
•    Validate asset ownership, relationships, and lifecycle status in collaboration with application and platform owners. 
•    Support ongoing audits to ensure AI assets and associated cost attribution remain accurate and current.

Knowledge/Skill Requirements

•    University degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent practical experience.
•    5-7 years of experience in platform operations, site reliability engineering, DevOps, cloud operations, or enterprise IT operations.
•    Strong experience supporting production platforms and services, including monitoring, incident response, problem management, service restoration, and operational reporting.
 

Technical Expertise:
•    Experience with cloud platforms, observability, automation, configuration management, and integration patterns, including Azure Automation runbooks (PowerShell/Python), Azure AI, Copilot integrations, AKS, virtual networks (hub-and-spoke), and App Service.
•    Expertise with observability tools such as Azure Monitor, Application Insights, and Grafana.
•    Experience with CI/CD and automation tools such as Azure DevOps, GitHub Actions, and Logic Apps.
•    Knowledge of configuration management and infrastructure-as-code tools such as Bicep, Terraform, Azure Policy, Key Vault, and relevant open-source technologies.
•    Knowledge of integration and event-driven technologies such as API Management, open-source API tools, Service Bus, Event Grid, and Apache Kafka.
•    Working knowledge of platform-supporting data and search services such as Elastic, Azure AI Search, and Cosmos DB.
•    Knowledge of enterprise network, edge security, and related internal platforms such as DNA, Fortinet, and Akamai is an asset.

Additional Capabilities:
•    Working knowledge of AI/ML operational concepts, including model lifecycle support, telemetry, governance controls, human-in-the-loop practices, and production monitoring.
•    Strong understanding of ITIL/ITSM processes, including change, release, incident, problem, configuration, and service reporting practices.
•    Analytical and structured thinker with strong troubleshooting, root-cause analysis, prioritization, and continuous improvement skills.
•    Strong service orientation, professional maturity, and the ability to collaborate effectively across operations, engineering, security, risk, data, and business teams.
•    Experience creating technical documentation, operational procedures, support playbooks, dashboards, and user guidance materials.
•    Knowledge of security, privacy, audit, and compliance considerations relevant to enterprise AI and platform operations.
Job Complexities / Thinking Challenges
This role requires balancing platform reliability, operational efficiency, and governance discipline in a rapidly evolving AI environment.