Staff DevOps Engineer: Remote Kubernetes Platform Lead

Jobicy; United States; Remote; USD 160,000 - 210,000; Full time

Benefits offered by this job

Health insurance

Life Insurance

Personal days

Vacation days

Christmas bonus

Company-matched savings fund

Job summary

Nextiva is seeking a Staff DevOps Engineer to lead the design and evolution of our platform engineering practice, with deep technical ownership of Kubernetes infrastructure.

This is an individual-contributor technical leadership role focused on defining platform architecture, standards, and paved roads to help engineering teams build, deploy, and operate software reliably and securely at scale, including AI/ML workloads.

Qualifications

• Bachelor’s degree in Computer Science or a related field, or equivalent work experience.
• 8+ years of DevOps, platform, or infrastructure engineering experience.
• 5+ years of hands-on Kubernetes experience in production, including cluster architecture, upgrades, and multi-tenant environments.
• Strong hands-on experience operating cloud infrastructure across AWS and Google Cloud Platform (GCP).
• Strong GitOps experience with Argo CD or Flux.
• Strong Infrastructure-as-Code experience with Terraform or Pulumi.
• Deep understanding of container orchestration, networking, ingress, and service mesh technologies such as Istio, Linkerd, or Cilium.
• Experience with middleware and messaging technologies such as Nginx, Kafka, and Redis at scale.
• Experience with GPU scheduling, node pools, and resource quotas supporting ML/AI training or inference workloads in GKE and EKS.
• Experience designing internal developer platforms or paved-road tooling for engineering organizations.
• Strong Linux, networking, storage, and security fundamentals.
• Experience operating observability platforms such as Prometheus, Grafana, Datadog, or OpenTelemetry at platform scale.
• Excellent communication skills with the ability to influence technical direction across teams.
• Demonstrated ability to understand, calculate, forecast, and optimize cloud and Kubernetes infrastructure costs, including evaluating the cost implications of architecture, capacity, compute, and resource-allocation decisions.
• Experience with capacity planning and resource optimization, balancing performance, reliability, scalability, and infrastructure cost.

Responsibilities

• Own the architecture and roadmap for our multi-cluster Kubernetes platform, including scaling, upgrades, and multi-tenancy.
• Establish GitOps-based deployment workflows using technologies such as Argo CD or Flux.
• Design and evolve an internal developer platform that provides engineering teams with self-service access to compute, environments, and observability.
• Architect service mesh, networking, and ingress strategies for reliable and secure communication across services.
• Define platform standards for GPU and ML workload scheduling and resource management on Kubernetes.
• Partner with AI/ML teams on infrastructure requirements for training and inference workloads.
• Drive capacity planning and cost optimization across Kubernetes and cloud infrastructure.
• Set technical direction and review architecture for platform-impacting changes across engineering.
• Define and own platform reliability through SLOs, incident-response leadership, and postmortems.
• Mentor experienced engineers and represent platform engineering in cross-organizational technical decisions.
• Influence teams toward common platform standards without relying on direct reporting authority.

Skills

Kubernetes

GitOps

Terraform

Pulumi

Istio/Service Mesh

Cloud (AWS/GCP)

Linux

Observability

Cost optimization

Communication

Education

Bachelor’s degree in Computer Science or related field

Equivalent work experience

Tools

Argo CD

Flux

Terraform

Pulumi

Nginx

Kafka

Redis

Job description

Nextiva is seeking a Staff DevOps Engineer to lead the design and evolution of our platform engineering practice, with deep technical ownership of Kubernetes infrastructure.

This is an individual-contributor technical leadership role focused on defining platform architecture, standards, and paved roads to help engineering teams build, deploy, and operate software reliably and securely at scale, including AI/ML workloads.