DevOps Engineer - KubeOps Team

About the team

KubeOps operates Wix's production Kubernetes platform at scale: a multi-DC EKS fleet with clusters running up to ~50,000 pods and ~2,000 nodes, hosting thousands of services across the company. We own the platform end-to-end - reliability, scale, upgrades, autoscaling and networking.

Job description

• Master the Cluster: Manage the lifecycle of our large-scale Kubernetes environments, without disrupting the thousands of services running on it.
• Build the Platform: Own and evolve the cluster building blocks - Kubernetes Architecture, Karpenter configurations, addons, networking, autoscaling. Where it makes sense, platformize them so other infra teams can consume them cleanly
• Pioneer AI-Driven Ops: Lead the charge in integrating AI into our ecosystem - building LLM-powered tools that accelerate investigation, planning, and coding for the whole team.
• Full-Stack Ownership: You'll take ambiguous tasks and transform them into high-quality outcomes, owning every decision along the way.

Requirements

• +2 years in managing infrastructure, operating production systems at large scale
• 2+ years running production Kubernetes - EKS preferred with a strong understanding of core control-plane and cluster components, with hands-on experience debugging, upgrading, and tuning autoscaling and cluster networking
• 2+ years with a major cloud provider (AWS preferred) - solid grasp of core compute, networking, and IAM.
• 2+ years with Infrastructure-as-Code, writing and maintaining reusable modules (Terraform, Pulumi, Crossplane, etc.)
• Familiarity with observability at scale - Prometheus, Grafana, alert design
• Experience building internal tooling in Go or Python
• Hands-on experience with AI-assisted development tools and agents (Claude Code, Codex, Cursor) for knowledge gathering, debugging, coding, planning, and design - with the ability to integrate these into concrete workflows that demonstrably accelerate your work.

Advantage

• Cluster autoscaling in production
• Cost / capacity optimization at fleet scale - Karpenter consolidation, bin-packing, spot strategy
• Linux internals and node-level debugging - when a Kubernetes problem turns into a Linux problem, you can take it from there
• Track record of leading projects end-to-end: scoping, execution and delivery.