Senior AI Platform Operations Engineer

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:
• Environment readiness,
• Dependency validation,
• Completion of operational readiness checklists,
• Structured service transition activities
• Support platform lifecycle management through:
• Release coordination,
• Change readiness validation,
• 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:
• Alert correlation,
• Anomaly detection,
• Root-cause support,
• Forecasting and predictive insights,
• 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:
• Usage and access controls,
• Data privacy considerations,
• Auditability and traceability,
• 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 suc…