> Markdown version of [/jobs/ext/4662458-senior-it-infrastructure-specialist](https://www.wearedevelopers.com/jobs/ext/4662458-senior-it-infrastructure-specialist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior IT Infrastructure Specialist - **Company:** Cohesity, Inc. - **Location:** Roeville, FL, United States - **Experience:** Expert - **Salary:** $101,320 - $126,650 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Build Automation, Ubuntu (Operating System), Cloud Computing, Cyber Security, Information Systems, Data Centers, Digital Architecture, Internet Security, Linux System Administration, Linux Servers, Log Analysis, NetApp Applications, Routing, Performance Tuning, Red Hat Enterprise Linux, Microsoft Copilot, Runbook, Security Information and Event Management, Virtual Local Area Networks, Virtualization Technology, Computer Network Operations, Large Language Models, Claude Code, AI Coding Agents, Pure Storage, Kubernetes, Information Technology, Data Management - **Published:** November 10, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-it-infrastructure-specialist-roseville-ca--c6f77dc3-f2a8-4f49-9f1f-ab4b30a1d7b7 ## About the Role Information Technology & Information Systems, Network Operations Center, Ecosystems, Internet Security, Cloud Computing, Artificial Intelligence (AI), Data Management, Security Information and Event Management (SIEM), Computer Security, Analysis Skills, * 5+ years of hands-on experience in Linux systems administration * Strong production experience with Kubernetes (deployment, operations, troubleshooting at scale) * Solid experience with kubevirt-based virtualization * Comfortable using AI tools (coding assistants, LLM-based troubleshooting/research) as part of daily engineering work * Proven experience operating production infrastructure supporting critical internal business services with defined uptime, performance, and recovery objectives. Preferred * SAN storage management experience with Pure Storage, Dell, HPE, and/or NetApp platforms * Datacenter networking experience (switching, routing, VLANs) * Experience building internal tooling or automation that incorporates AI/LLM APIs ## Description * Design, deploy, and maintain Linux server infrastructure (RHEL/Rocky/Ubuntu and similar) across production and non-production environments * Operate and scale Kubernetes clusters: deployments, networking, storage integration, upgrades, and troubleshooting * Manage kubevirt-based virtualization environments: VM lifecycle, resource allocation, live migration, performance tuning, and host maintenance * Administer and optimize SAN storage systems (Pure Storage, Dell, HPE, and/or NetApp), including provisioning, performance, snapshots, and capacity planning * Contribute to datacenter networking design and troubleshooting (VLANs, switching, routing fundamentals) where applicable * Build automation and tooling to reduce repetitive manual work across the infrastructure stack * Participate in on-call rotation and incident response, driving root-cause analysis for infrastructure issues * Document architecture, runbooks, and operational procedures * Participate in maintenance activities, platform upgrades, and infrastructure migrations across global data center locations, * Scripting & automation: Using AI coding assistants (e.g., Claude Code, Copilot) to draft and refine automation for provisioning, patching, and cluster operations; reviewing and validating output rather than blindly trusting it * Kubernetes operations: Using AI to help generate and audit services and troubleshoot pod/network/storage issues faster by summarizing logs and correlating events across clusters * Incident response: Using AI to accelerate log analysis, correlate symptoms across systems (compute, storage, network) during outages, and draft initial incident timelines/postmortems * Capacity & performance analysis: Using AI-assisted data analysis to identify SAN storage trends, VM resource pressure, or cluster bottlenecks before they become incidents * Documentation: Using AI to turn tribal knowledge into clear runbooks, architecture diagrams, and onboarding docs, keeping documentation current with less manual overhead * Knowledge gaps: Using AI as a first-pass research tool for unfamiliar storage arrays, network gear, or Kubernetes add-ons, then validating against vendor docs and testing in a safe environment We care about outcomes and judgment, not tool usage for its own sake. You should be comfortable explaining why an AI-suggested command, config, or fix is correct before running it in production.