AI Platform Operations Manager

THE

COMPANY: STACK INFRASTRUCTURE (STACK) provides digital infrastructure to scale the world’s most innovative companies. We are an award-winning industry leader in building, owning, and operating highly efficient, cost-effective wholesale, colocation, and cloud data centers. Each of our national facilities meets or exceeds the highest industry standards in all operational categories of availability, security, connectivity, and physical resilience. STACK offers the scale and geographic reach that rapidly growing hyperscale and enterprise companies need. The world runs on data. Data runs on STACK. THE POSITION: The DevOps Engineer, AI Platform is responsible for automating, deploying, and operating the infrastructure and delivery pipelines that support STACK’s enterprise AI platform on Azure. This is a hands-on engineering role focused on build and run — not oversight. Reporting to Head of AI, Enterprise AI & Data Strategy org this individual owns the infrastructure-as-code, CI/CD, containerization, observability, and release automation that allow AI engineers and enterprise application teams to ship agentic AI solutions, RAG pipelines, and integration services reliably and repeatably.

The role sits at the intersection of cloud infrastructure, platform engineering, and MLOps — turning platform architecture into automated, governed, observable, and cost-efficient environments that teams across the organization build on.

KEY RESPONSIBILITIES Infrastructure Automation & Infrastructure as Code Build, maintain, and version infrastructure-as-code modules for Azure environments using Terraform, Bicep, or ARM, including compute, networking, storage, identity, and AI platform resources. Automate provisioning of AI platform components — Azure AI Foundry, Azure OpenAI Service, Azure AI Search, Cosmos DB, ADLS Gen2, and Databricks — as reusable, parameterized deployment patterns. Maintain environment parity across development, test, and production, including configuration management, drift detection, and remediation. Implement and enforce tagging, naming, and resource organization standards that support governance, chargeback, and lifecycle management. Automate routine platform operations — patching, certificate rotation, key and secret rotation, backup validation, and disaster recovery testing. CI/CD & Release Engineering Design, build, and operate CI/CD pipelines in Azure DevOps or GitHub Actions for application code, infrastructure code, container images, and AI/agent deployments. Implement automated build, test, security scanning, artifact management, and promotion gates across environments. Establish branching strategies, code review standards, and release management practices in partnership with AI engineering and enterprise application teams. Build deployment automation for agentic AI services, MCP (Model Context Protocol) servers, and integration workloads running on Azure Container Apps and Azure Kubernetes Service (AKS). Support model and prompt release workflows — versioning, staged rollout, evaluation gates, and rollback procedures for LLM-based applications. Container Platform & AI Workload Operations Operate and tune AKS and Azure Container Apps, including cluster upgrades, node pool sizing, autoscaling, ingress, networking, and workload isolation. Build and maintain container images, base image standards, and registry governance in Azure Container Registry. Manage compute scheduling and scaling for AI workloads, including GPU-backed and inference-heavy workloads where required. Implement resiliency patterns — health probes, retries, throttling, quota management, and failover — for AI endpoints and integration services. Observability, Reliability & Incident Response Instrument platform and AI services with logging, metrics, tracing, and alerting using Azure Monitor, Log Analytics, Application Insights, and equivalent open-source tooling. Build dashboards and service-level indicators covering platform availability, latency, throughput, error rates, token consumption, and model endpoint performance. Participate in on-call rotation, lead incident triage and resolution for platform issues, and drive root cause analysis and corrective actions. Develop and maintain runbooks, operational documentation, and automated remediation for recurring issues. Security, Governance & Cost Optimization Implement DevSecOps practices — secrets management in Azure Key Vault, managed identity usage, least-privilege access, dependency and container vulnerability scanning, and policy-as-code. Partner with Information Security to ensure pipelines and environments meet enterprise security, data residency, and compliance

requirements. Support Azure FinOps practices through cost visibility, rightsizing, reserved capacity, and automated controls on non-production and idle resources. Maintain audit trails and change records for infrastructure and release activity. Delivery & Cross-Functional Collaboration Work directly with AI engineers, data engineers, and enterprise application teams to remove deployment friction and improve time-to-production for AI solutions. Translate platform architecture and standards into automated, self-service capabilities that teams can consume without deep infrastructure knowledge. Contribute to platform engineering standards, reference implementations, and internal documentation. Provide technical escalation support for build, deployment, and environment issues. THE DETAILS:

Location: Denver, CO Travel: <10%

Benefits: Healthcare, Dental Care, Vision Insurance, Life Insurance, Paid Time Off, and Paid Leave Programs Must be eligible to work in the United States Must pass comprehensive background and drug screening MUST-HAVE

QUALIFICATIONS

Bachelor's degree in Computer Science, Information Technology, Engineering, or related field, or equivalent practical experience. 5+ years of hands-on experience in DevOps, site reliability engineering, or platform engineering roles with a strong delivery