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Role description

Technical Support Engineer

Rafay logoRafay
TypeFull-time
SenioritySenior
LocationUnited States
Posted10/30/2026
Customer Success

About Rafay Systems

Rafay is redefining how enterprises and GPU cloud providers deploy, manage, and scale modern applications. Our platform delivers self-service workflows, multi-cluster orchestration, and end-to-end life cycle management to Kubernetes and cloud-native infrastructure—empowering platform teams to operate with speed, security and efficiency at scale.

As we grow, we’re looking for a Technical Support Engineer who thrives on solving complex distributed systems problems and is passionate about delivering a world-class support experience.

Role Summary

This is a
deeply technical, hands-on support role
focused on diagnosing and resolving real-world production issues in Kubernetes environments. This is not a ticket triage role—you’ll be expected to
own problems end-to-end
.

You’ll work directly with enterprise customers running mission-critical workloads, acting as a
technical escalation point
across Kubernetes control planes, cluster lifecycle operations, networking, and cloud infrastructure. You’ll collaborate closely with Engineering and SRE teams to debug issues, identify root causes, and drive resolution—not just workaround symptoms.

This role offers a unique opportunity to work at the cutting edge of Kubernetes, cloud infrastructure, and AI/ML platform management, while collaborating with our Customer Success and Engineering teams to ensure successful customer outcomes.

Key Responsibilities

  • Own and resolve
    advanced technical support cases
    involving multi-cluster Kubernetes deployments, cluster provisioning failures, and workload runtime issues across public/private clouds
  • Perform deep troubleshooting using tools like kubectl, cluster logs, events, and metrics to diagnose issues across control plane and data plane components
  • Debug and support
    cluster lifecycle management
    workflows including provisioning, upgrades, scaling, and recovery.
  • Analyze issues related to
    networking (CNI), ingress, DNS, service mesh, and storage (CSI)
    in Kubernetes environments
  • Reproduce complex customer issues in internal environments and identify
    root cause with precision
  • Act as a trusted customer advocate—proactively identifying risks and working cross-functionally to resolve them. Collaborate with Engineering to escalate bugs, validate fixes, and improve product reliability
  • Provide clear, concise, and technically accurate communication to customers during incident resolution
  • Contribute to
    runbooks, troubleshooting guides, and knowledge base articles
  • Stay up to date on Rafay platform features, releases, and cloud-native ecosystem updates.
  • Participate in
    on-call rotations
    to support critical customer incidents

Required Qualifications

  • 5+ years of experience in Technical Support, SRE, or DevOps roles supporting production environments
  • Strong hands-on experience managing and troubleshooting Kubernetes clusters in production
  • Deep expertise with Kubernetes architecture, container orchestration technologies and debugging techniques
  • Proven ability to troubleshoot Pod lifecycle issues, Cluster networking (DNS, Routing, Firewalls etc.), Storage, Helm deployments and Node-level issues
  • Strong understanding of cloud platforms: AWS, GCP, or Azure and virtualization technologies (vSphere, OpenStack)
  • Solid fundamentals in Linux systems, networking (TCP/IP, DNS), and distributed systems
  • Experience working in customer-facing roles, handling escalations and high-severity incidents, experience with support tools like Zendesk.
  • Excellent written and verbal communication skills
  • Proven ability to work independently in fast-paced, dynamic environments.
  • Bachelor’s degree in computer science or related field (or equivalent practical experience).

Preferred Qualifications

  • CKA (Certified Kubernetes Administrator) or equivalent hands-on expertise
  • Experience with Kubernetes ecosystem tools such as Helm, Prometheus, Grafana, and Terraform
  • Familiarity with multi-cluster management and GitOps workflows
  • Experience supporting enterprise SaaS platforms or developer infrastructure products
  • Exposure to AI/ML infrastructure or GPU-based workloads is a plus

Why Join Rafay?

Rafay is at the forefront of GPU PaaS technologies and Kubernetes and we offer unique opportunities to join a winning team working on foundational technology for cloud and AI/ML services and enterprises. We work in a collaborative environment that rewards creative thinking and provides opportunities to advance professional careers in advanced technology development. On top of this we offer a fun and dynamic work environment, a competitive salary, robust benefits and attractive stock options. As the first of our kind, we are truly in a class of our own.

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Company info

Rafay logo

Rafay

Rafay delivers an enterprise PaaS for modern infrastructure and accelerated computing. Platform teams at many of the largest enterprises in the world use Rafay’s platform to deliver AS-A-SERVICE consumption of compute to their internal customers. Whether developers and data scientists need to deploy CPU-based and GPU-based workloads in data centers, public clouds, or at the edge, Rafay makes it easy.

Series BIn the news
HQSunnyvale, US
Size241 employees
IndustrySoftware Development
Unlock human wisdom
ThesisTermsPrivacy
Funding
  • Series B · Aug 31, 2021
Tech stackapollo-io, outreach, openstack, nvidia-dynamo, linkedin-campaign-manager, remote-ok, runai, elearning-lms, linkedin-recruiter, zendesk
News
  • Rafay and Fortanix Extend Confidential AI as a Service to Kubernetes Pods
  • Rafay Adds Confidential AI Orchestration for Kubernetes Pods
  • Rafay and Fortanix Help Neoclouds, Sovereign AI Clouds and Enterprises Deliver Confidential AI as a Service
  • Rafay’s playbook on helping neoclouds graduate beyond selling just infrastructure
  • Rafay Adds Token Router Capabilities to Token Factory, Putting Frontier and Open-source Models Under One Spending Policy with Granular Token Budgets

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