There's a fully remote Kubernetes engineer role open, paying $138,000 a year to candidates anywhere in the world. It's a full-time position in DevOps and cloud infrastructure, focused specifically on the containerized systems that most modern applications run on.
Kubernetes has become the default way many companies run containerized workloads at scale, but running it well requires ongoing, dedicated attention rather than a one-time setup. This role exists for that ongoing work: keeping clusters healthy, efficient, and able to handle whatever the applications running on them throw at them.
What you'd be doing
- Design, deploy, and manage containerized application infrastructure
- Optimize cluster performance and scaling
- Troubleshoot orchestration issues
Orchestration problems in Kubernetes rarely announce themselves with a clear error message. A rolling deployment can get stuck partway through because a readiness probe never reports healthy, leaving old and new pods running side by side indefinitely while the rollout silently stalls. Figuring out whether that's an actual application problem or just a probe configured too aggressively takes real hands-on debugging, not a quick glance at a dashboard.
Scaling work goes beyond simply increasing the replica count as traffic increases. A cluster that scales pods without considering how those pods are actually distributed across nodes can end up with several resource-hungry pods packed onto one node while others sit nearly idle, and that kind of imbalance shows up as mysterious performance issues that have nothing to do with the application code itself.
Networking within a cluster presents its own set of problems that don't map cleanly onto traditional networking knowledge. DNS resolution between services can degrade intermittently under load in ways that are hard to reproduce on demand, and tracing that kind of issue back to its root cause, whether it's a misconfigured CoreDNS deployment or a node running low on resources, takes patience and a systematic approach rather than guesswork.
What's expected
This one calls for a bachelor's degree, and computer science is what shows up most often on resumes that make it through, though the degree itself matters less here than a demonstrated production Kubernetes track record. Candidates need three years of hands-on experience deploying and managing Kubernetes clusters in production, and a certification such as CKA is commonly valued, though hands-on experience carries more weight in the review than the credential alone.
- Kubernetes
- Docker
- Container orchestration
- Helm
- Cloud platforms
- Infrastructure as code
- Monitoring and logging tools
CKAD and CKS certifications, focused on application development and security respectively, tend to stand out alongside the more foundational CKA. Hands-on experience with a service mesh like Istio or Linkerd is worth mentioning, as is any background in writing custom operators or working with Custom Resource Definitions, since that kind of work signals a deeper level of Kubernetes fluency than cluster administration alone.
Multi-cluster management experience is increasingly relevant as well, since companies running Kubernetes at scale often operate several clusters across regions or environments rather than a single cluster. Managing consistency across multiple clusters, keeping configurations and policies aligned without manually replicating changes everywhere, is a skill that only develops through actually doing it.
Pay and benefits
The salary is $138,000 annually. Certification reimbursement comes standard with health insurance, paid time off, and 401(k) matching, which matters in a field where CKAs and related certifications require periodic renewal. A number of employers hiring Kubernetes engineers at this level also offer an employee assistance program supporting mental health and general wellbeing.
- Certification reimbursement
- Health insurance
- Paid time off
- 401(k) matching
The real complexity of running Kubernetes
Kubernetes has a reputation for being genuinely difficult to run well, and that reputation is earned. Naukri Mitra sees this come up constantly in candidate conversations: plenty of engineers can deploy a simple application to a cluster, but far fewer can debug why a specific pod keeps getting evicted under memory pressure or why DNS resolution intermittently fails inside the cluster network. That gap between basic familiarity and genuine production troubleshooting is exactly what this role's experience requirement is meant to filter for.
GitOps workflows, typically via a tool like ArgoCD or Flux, are common on teams running Kubernetes at any real scale, since manually applying changes to a cluster becomes unreliable once more than a couple of engineers are making them. Comfort working within that kind of declarative, git-driven deployment model tends to matter more day-to-day than people expect when coming in.
Cost management is a growing part of Kubernetes work, too, since an over-provisioned cluster can quietly burn through budget while still appearing perfectly healthy on every dashboard. Tools like the cluster autoscaler, or newer options built specifically around efficient node scaling, only help if someone's actually tuning their configuration rather than leaving default settings in place indefinitely.
Getting there and applying
The remote salary for a Kubernetes engineer at this level reflects genuine scarcity. Most engineers learning to become remote Kubernetes engineers come up through general DevOps or backend roles first, picking up container orchestration skills as their teams adopt Kubernetes, rather than specializing in it from day one. Three years of hands-on production experience, the bar for this role, tends to separate candidates who've only worked in managed, simplified Kubernetes environments from those who've handled the messier realities of running it at scale.
The technical conversation for a role like this usually centers on a specific cluster problem, walking through diagnosis rather than reciting Kubernetes concepts from memory. Someone who can describe exactly how they'd narrow down a pod stuck in a bad state, checking events, logs, and resource limits in a logical order, will generally come across stronger than someone who can only define terms like readiness probes and horizontal pod autoscaling in the abstract.
