Senior AI Platform Operations Engineer
Equitable BankJobsCloseBy Editorial Insights
Equitable Bank seeks a Senior AI Platform Operations Engineer to own the reliability, operability and controlled enablement of its AI platform, ensuring production readiness, security, observability and compliance across management, incident coordination and governance. You will enable safe, scalable AI adoption by driving operational readiness, incident response and post incident reviews, and by advancing automation and AI Ops for anomaly detection and forecasting. Bring 5 to 7 years in cloud or platform operations with Azure tools (AKS, Monitor, Application Insights), CI/CD (Azure DevOps, GitHub Actions), and IaC (Terraform, Bicep). Tailor your resume to measurable outcomes, incident metrics, audit artifacts and cross functional collaboration; emphasize security, privacy and governance, and confirm onsite availability in Toronto.
Purpose of Job
The Senior AI Platform Operations Engineer is accountable for the reliability, operability, and controlled enablement of the organization’s AI platform.
This role ensures that AI Platform services and solutions are production-ready, secure, observable, and compliant by executing disciplined operational practices, across platform management, monitoring, incident coordination, and governance control enforcement.
The incumbent plays a key role in enabling the safe and scalable adoption of AI by ensuring that AI solutions are deployed, monitored, supported, and continuously improved in line with enterprise standards for reliability, security, and compliance
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
• 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.