AI Platform Operations Manager

STACK Infrastructure•Denver, CO

13d•$128,260 - $146,018•Onsite

About 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.

Requirements

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 track record.
Strong proficiency in Infrastructure as Code — Terraform, Bicep, or ARM — including module design, state management, and reusable patterns.
Proven experience building and operating CI/CD pipelines in Azure DevOps or GitHub Actions.
Hands-on experience with containerization and orchestration — Docker, Azure Kubernetes Service (AKS), and Azure Container Apps or equivalent.
Solid working knowledge of Azure core services — compute, networking (VNet, NSG, Private Endpoints), storage, identity (Entra ID), and Key Vault.
Strong scripting and automation skills in Python, PowerShell, or Bash.
Experience with monitoring and observability tooling — Azure Monitor, Log Analytics, Application Insights, Prometheus, or Grafana.
Working knowledge of Git-based workflows, code review practices, and artifact/registry management.
Demonstrated ability to troubleshoot production issues across infrastructure, network, and application layers.

Nice To Haves

Microsoft Certified: DevOps Engineer Expert (AZ-400), Azure Administrator (AZ-104), or Certified Kubernetes Administrator (CKA).
Experience deploying and operating AI/ML workloads — model endpoints, RAG pipelines, vector databases, or agentic services in production.
Familiarity with MLOps tooling and practices — Azure Machine Learning, MLflow, Databricks, or equivalent model lifecycle platforms.
Experience deploying MCP (Model Context Protocol) servers or similar integration services connecting AI agents to enterprise systems.
Exposure to agentic AI frameworks such as Semantic Kernel, LangGraph, or AutoGen from a deployment and operations perspective.
Experience with GPU compute provisioning, quota management, and inference cost optimization.
Knowledge of FinOps frameworks and Azure cost optimization practices.
Experience integrating with enterprise systems such as Microsoft 365, Freshworks ITSM, Workday, NetSuite, or Procore.
Experience in data center, hyperscale, or infrastructure-intensive industry environments.

Responsibilities

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.
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.
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.
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.
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.
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.