• 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.

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

• Expected to become an active contributor to production infrastructure operations within the first 60-90 days, including participation in incident response, operational reviews, and infrastructure change management
• Operate across multiple technology domains.
• Incidents are resolved faster because AI-assisted analysis shortens the diagnostic loop
• Manual, repetitive operational work steadily decreases as automation (AI-assisted or otherwise) takes it over
• The team’s institutional knowledge is captured in living documentation rather than a single person’s head

LI-EM1

Disclosure Pursuant to Applicable State Equal Pay Transparency Laws - This position has a starting pay range as listed below. Actual salary depends upon many factors, including a candidate’s skills, qualifications and experience, location, and salary expectations, and therefore a starting salary at the low end, high end, or even above the stated range may be offered. This position may also be eligible for bonus compensation, commission (if in a sales function), and/or equity grants. Additionally, full-time employees are eligible to participate in our comprehensive benefits framework, including health and wellness benefits, vacation, paid holidays and refresh days, 401(k) retirement plan, life and disability insurance coverages, and other benefits the Company may offer from time to time.

Pay Range : $101,320.00-$126,650.00

The compensation noted above is based on an annualized hourly rate assuming normal full-time employment.

Cohesity is a leader in AI-powered data security and management. Aided by an extensive ecosystem of partners, Cohesity makes it easy to secure, protect, manage, and get value from data - across the data center, edge, and cloud. Cohesity helps organizations defend against cybersecurity threats with comprehensive data security and management capabilities, including immutable backup snapshots, AI-based threat detection, monitoring for malicious behavior, and rapid recovery at scale.

We’ve been named a Leader by multiple analyst firms and have been globally recognized for Innovation, Product Strength, and Simplicity in Design.

Join us on our mission to shape the future of our industry.

We are seeking a Senior IT Infrastructure engineer who thrives in dynamic environments and can rapidly learn new platforms, embrace AI-assisted operations, and contribute across both lab and production infrastructure. Success in this role requires strong technical depth, operational ownership, collaboration, and a bias toward automation and continuous improvement. You’ll work across Linux systems, Kubernetes clusters, kubevirt-based virtualization, and enterprise SAN storage. You will be expected to use AI tools to move faster, reduce toil, and catch issues earlier than a purely manual workflow would allow.

This is not an “AI-optional” role. We expect modern infrastructure engineers to pair deep systems knowledge with AI-assisted workflows for scripting, troubleshooting, documentation, and operational analysis. The judgment and systems expertise are still yours; AI is a force multiplier, not a replacement for understanding what’s happening under the hood., Cohesity employees who are within a reasonable commute (e.g. within a forty-five (45) minute average travel time) work out of our core offices 2-3 days a week of their choosing.