Lead AI Platform Engineer
Leads the engineering, reliability, and production operations of enterprise AI platforms on Azure: platform design, enablement, observability, automation, and governance controls. Core stack includes AKS, Azure Monitor/Application Insights, Grafana, Azure DevOps/GitHub Actions, Terraform/Bicep, Kafka, and Azure AI/Copilot services.
- Compensation
- Not disclosed
- Location
- Toronto, Canada
- Work setup
- Not specified
- Seniority
- Lead
- Employment
- Not specified
- Experience
- 7+ years
- Category
- Devops
- Posted
Skills
Must have
- University degree in Computer Science, Engineering, IT, or related field, or equivalent practical experience
- 7+ years in platform engineering, SRE, DevOps, cloud operations, enterprise IT operations, or production platform support
- Experience leading technical delivery, engineering standards, production readiness, incident response, problem management, and service restoration
- Advanced experience with cloud platforms, observability, automation, configuration management, and integration patterns (Azure Automation runbooks, Azure AI, Copilot integrations, AKS, virtual network
- Strong expertise with observability tools (Azure Monitor, Application Insights, Log Analytics, Grafana, dashboards, alerting)
- Strong experience with CI/CD and infrastructure-as-code (Azure DevOps, GitHub Actions, Logic Apps, Bicep, Terraform, Azure Policy, Key Vault)
- Knowledge of integration and event-driven technologies (API Management, Service Bus, Event Grid, Apache Kafka)
- Working knowledge of data and search services (Elastic, Azure AI Search, Cosmos DB)
- Strong working knowledge of AI/ML operational concepts: model lifecycle support, platform telemetry, governance controls, human-in-the-loop, and production monitoring
- Strong understanding of ITIL/ITSM processes (change, release, incident, problem, configuration, service reporting, operational risk)
- Proven technical leadership: mentoring engineers, influencing standards, guiding implementation decisions, coordinating cross-functional delivery
Nice to have
- Knowledge of enterprise network, edge security, identity, and access management platforms such as DNA, Fortinet, and Akamai
Full description
Purpose of the Job:
The Lead AI Platform Engineer is accountable for technical leadership, engineering excellence, reliability, operability, and the controlled enablement of the organization’s enterprise AI platforms.
This role provides hands-on technical leadership across AI platform design, implementation, automation, observability, and production readiness. The incumbent ensures AI platform services and solutions are secure, resilient, observable, supportable, and compliant with enterprise standards for reliability, security, platform management, monitoring, incident coordination, and governance control enforcement.
The incumbent acts as a senior technical lead for platform engineering activities, guiding implementation decisions, establishing engineering patterns, mentoring team members, and partnering with cross-functional stakeholders to enable the safe and scalable adoption of AI across the enterprise.
Main Activities:
AI Platform Engineering Leadership, Reliability and Operations
- 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.