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Cloud DevOps EngineereTeam • Toronto, ON
Cloud DevOps Engineer

Cloud DevOps Engineer

eTeam • Toronto, ON
14 hours ago
Job type
  • Full-time
Job description
Role Name: Cloud DevOps Engineer
Work site: Toronto (Onsite)
Senior DevOps / Site Reliability Engineer – AI Platform

Role Overview:
We are looking for a Senior DevOps / Site Reliability Engineerto help build, operate, monitor, and scale our new enterprise AI platform.
This role will be responsible for the DevOps and reliability capabilities supporting the platform across all environments, including Development, QA, and Production. The environment is currently focused and manageable, consisting of approximately – containers, but is expected to grow as the AI platform expands in and beyond.
This is an opportunity to work with a collaborative team on a new platform where the focus is not simply deploying infrastructure. The most important part of the role will be creating intelligent dashboards, monitoring, alerting, automated scaling, and AI-assisted platform management.
The platform is hosted primarily in Microsoft Azure, with limited exposure to AWS.

Key Responsibilities:
Azure Infrastructure and Platform Deployment:
  • Design, deploy, configure, and maintain infrastructure within Microsoft Azure.
  • Deploy and manage virtual machines, containers, Kubernetes clusters, networking, storage, and supporting platform services.
  • Support infrastructure across Development, QA, and Production environments.
  • Establish repeatable and reliable deployment processes using Infrastructure as Code and CI/CD automation.
  • Maintain secure, resilient, and appropriately sized platform environments.

Kubernetes and Container Management:
  • Deploy, configure, and operate containerized applications using Kubernetes.
  • Manage container lifecycle, configuration, secrets, networking, storage, and application dependencies.
  • Monitor container and cluster health, resource consumption, capacity, and performance.
  • Troubleshoot deployment, networking, configuration, and runtime issues.
  • Establish appropriate standards for container deployment and Kubernetes operations.

Performance, Load Management, and Scaling:
  • Monitor platform demand, workload patterns, resource utilization, and application performance.
  • Configure horizontal and vertical scaling policies for containers and supporting infrastructure.
  • Develop intelligent scaling approaches based on workload, queue depth, response time, resource utilization, and business demand.
  • Conduct capacity planning and identify potential performance bottlenecks before they affect production.
  • Help introduce predictive or AI-assisted scaling and platform management capabilities.

Dashboards and Platform Visibility:
  • Design and build advanced operational dashboards using tools such as Grafana, Kibana, Azure Monitor, Application Insights, and similar technologies.
  • Create clear executive, operational, application, and infrastructure views of platform health.
  • Build dashboards covering availability, performance, capacity, errors, latency, traffic, container health, AI workloads, and service dependencies.
  • Establish meaningful service-level indicators, service-level objectives, and reliability metrics.
  • Continuously improve dashboards so that issues, trends, and risks can be quickly identified.
  • Advanced dashboard design and dashboard-building experience is a core requirement for this role.

Monitoring and Alerting:
  • Implement monitoring and alerting across infrastructure, applications, containers, integrations, and AI platform services.
  • Configure actionable alerts that identify real production risks while minimizing unnecessary alert noise.
  • Establish thresholds, anomaly detection, health checks, synthetic monitoring, and automated remediation where appropriate.
  • Create operational runbooks and troubleshooting guidance.
  • Work with development and architecture teams to improve platform observability.

Production Reliability and Support:
  • Support the stability, availability, and operational readiness of the production AI platform.
  • Investigate and resolve platform, deployment, infrastructure, monitoring, and performance issues.
  • Participate in root-cause analysis and implement preventative improvements.
  • Ensure that production support processes, documentation, and escalation paths are established before platform usage increases.
  • Provide very light production support during , with no regular after-hours support currently anticipated.
  • Help prepare the operating model for increased platform adoption and support requirements expected in .

Required Qualifications:
  • Strong professional experience in DevOps, Site Reliability Engineering, cloud infrastructure, or platform engineering.
  • Advanced hands-on experience with Microsoft Azure.
  • Strong experience deploying and operating Kubernetes environments.
  • Strong knowledge of containerization technologies such as Docker.
  • Experience deploying and supporting containerized applications in Development, QA, and Production environments.
  • Advanced experience designing and building dashboards using Grafana, Kibana, Azure Monitor, Application Insights, or comparable tools.
  • Strong experience implementing monitoring, observability, logging, alerting, and operational health checks.
  • Experience managing application load, infrastructure capacity, performance, and automated scaling.
  • Experience with CI/CD pipelines and automated application deployment.
  • Experience with Infrastructure as Code tools such as Terraform, Bicep, or ARM templates.
  • Strong troubleshooting skills across applications, containers, infrastructure, networking, and cloud services.
  • Ability to work independently while collaborating closely with developers, architects, AI engineers, and platform stakeholders.

Preferred Qualifications:
  • Experience supporting AI, machine learning, data, or high-compute platforms.
  • Experience monitoring AI models, inference services, token usage, GPU workloads, API consumption, queues, or model performance.
  • Experience implementing automated remediation, predictive monitoring, or AI-assisted platform operations.
  • Familiarity with AWS services and cloud operations.
  • Experience with Elasticsearch, Log Analytics, OpenTelemetry, Prometheus, or similar observability technologies.
  • Experience defining service-level indicators, service-level objectives, and reliability standards.
  • Experience with security, identity, secrets management, and cloud governance within Azure.

What Makes This Role Different:
  • This is not a large-scale, high-pressure production support environment. The initial platform footprint is relatively focused, with approximately – containers and very limited production support expected during .
  • The role offers the opportunity to establish the platform correctly from the beginning, introduce modern DevOps and SRE practices, and experiment with intelligent monitoring, automated scaling, advanced dashboards, and AI-assisted platform management.
  • As platform adoption increases, the responsibilities and operational scope are expected to grow throughout .

Ideal Candidate:
  • The ideal candidate is a senior, hands-on DevOps or SRE professional who enjoys building reliable cloud platforms but is equally interested in observability, intelligent automation, dashboards, and operational innovation.
  • They should be comfortable working in a new and evolving environment, establishing practical standards without unnecessary complexity, and helping a collaborative team build a modern enterprise AI platform.
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Cloud DevOps Engineer • Toronto, ON

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