Lead AI Platform Engineer

• Lead the engineering, configuration, and operation of enterprise AI platforms to ensure availability, performance, resilience, and scalability across environments, integrations, and supporting infrastructure.
• Define and implement platform engineering patterns, standards, reusable components, and operational guardrails that support secure and reliable AI solution delivery.
• Provide technical leadership for platform triage, incident resolution, escalation coordination, and post-incident reviews to strengthen service stability and resilience.
• Track and report on service reliability indicators, incident trends, engineering risks, and operational performance improvements.

AI Platform Enablement, Solution Readiness, Production Readiness and Technical Delivery

• Lead technical enablement of approved AI use cases into non-production and production environments by ensuring environment readiness, dependency validation, release readiness, operational supportability, and service transition planning.
• Partner with architecture, security, cloud, infrastructure, delivery, and application teams to translate solution requirements into secure, supportable, and scalable platform implementations.
• Guide platform lifecycle management through release coordination, change readiness validation, maintenance planning, capacity planning, and technical risk mitigation.
• Ensure AI platform changes meet defined engineering, operational, security, and control readiness criteria prior to release.

Observability, Automation and AI Ops Engineering

• Design, implement, and continuously improve observability capabilities, including telemetry, logging, metrics, traces, dashboards, and alerting required for enterprise AI operations.
• Lead automation initiatives using approved tools and practices to reduce manual effort, improve reliability, and standardize repeatable operational activities.
• Analyze operational data to identify anomalies, recurring issues, root-cause patterns, performance bottlenecks, and opportunities for proactive service improvement.
• Implement AI Ops use cases such as alert correlation, anomaly detection, forecasting, root-cause support, knowledge retrieval, and automation of repetitive operational tasks.
• Mentor engineers on observability, automation, troubleshooting, and service reliability practices.

Governance, Risk and Control Engineering Execution

• Embed governance, security, privacy, auditability, traceability, and human oversight requirements into AI platform engineering and operational practices.
• Ensure platform implementations align with enterprise security policies, risk controls, compliance requirements, architecture standards, and operational readiness expectations.
• Partner with security, risk, compliance, architecture, and data teams to assess implementation risks, close control gaps, and enable responsible deployment of AI capabilities.
• Maintain documentation and evidence required for audit, governance reviews, production readiness checkpoints, and control validation.
• Identify technical and control risks, recommend mitigation options, and escalate appropriately to relevant governance and risk stakeholders.

AI Asset Visibility, Platform Integrity, Operational Integrity and Engineering Standards

• Maintain engineering and operational visibility of AI platform assets required for monitoring, support, ownership, lifecycle management, and cost alignment.
• Validate asset ownership, relationships, configuration integrity, and lifecycle status in collaboration with application, platform, architecture, and infrastructure owners.
• Establish and promote engineering standards, reusable patterns, documentation, and technical practices that improve platform supportability and operational integrity.

• University degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent practical experience.
• 7+ years of experience in platform engineering, site reliability engineering, DevOps, cloud operations, enterprise IT operations, or production platform support.
• Demonstrated experience leading technical delivery, engineering standards, production readiness, incident response, problem management, service restoration, and operational reporting for enterprise platforms.

Technical Expertise:

• Advanced experience with cloud platforms, observability, automation, configuration management, and integration patterns, including Azure Automation runbooks, Azure AI, Copilot integrations, AKS, virtual networks, App Service, and supporting Azure services.
• Strong expertise with observability tools such as Azure Monitor, Application Insights, Log Analytics, Grafana, dashboards, alerting, and operational telemetry design.
• Strong experience with CI/CD, automation, and infrastructure-as-code tools such as Azure DevOps, GitHub Actions, Logic Apps, Bicep, Terraform, Azure Policy, Key Vault, and related 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, Cosmos DB, and related data platform capabilities.
• Knowledge of enterprise network, edge security, identity, access management, and related internal platforms such as DNA, Fortinet, and Akamai is an asset.

Additional Capabilities:

• Strong working knowledge of AI/ML operational concepts, including model lifecycle support, platform telemetry, governance controls, human-in-the-loop practices, responsible AI considerations, and production monitoring.
• Strong understanding of ITIL/ITSM processes, including change, release, incident, problem, configuration, service reporting, and operational risk practices.
• Proven ability to provide technical leadership, mentor engineers, influence standards, guide implementation decisions, and coordinate complex cross-functional delivery.
• Analytical and structured thinker with advanced troubleshooting, root-cause analysis, prioritization, risk assessment, and continuous improvement skills.
• Strong service orientation, professional maturity, and the ability to collaborate effectively across operations, engineering, security, risk, data, architecture, and business teams.
• Experience creating technical documentation, engineering patterns, operational procedures, support playbooks, dashboards, and user guidance materials.