Description

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 2026, with no regular after-hours support currently anticipated.
• Help prepare the operating model for increased platform adoption and support requirements expected in 2027.

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

Key Skills

Education

Bachelor's degree