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.