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

Technical Expertise:

• Experience with cloud platforms, observability, automation, configuration management, and integration patterns, including Azure Automation runbooks (Power Shell/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 Dev Ops, Git Hub 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…