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Kubernetes is the standard for scaling AI workloads in production. RemoteAI connects you with Kubernetes engineers who have deployed and managed ML model serving, GPU clusters, and data pipelines at scale.
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Kubernetes is the standard for scaling AI workloads in production. RemoteAI connects you with Kubernetes engineers who have deployed and managed ML model serving, GPU clusters, and data pipelines at scale.
Kubernetes engineers design cluster architecture, configure deployments and services, set up auto-scaling for variable AI workloads, manage GPU node pools, implement Helm charts, and configure monitoring with Prometheus and Grafana. In AI contexts, they run Kubeflow, KServe, and Ray clusters.
Finding the right kubernetes engineer requires more than evaluating technical skills — it means identifying someone who ships production-ready work and understands your business goals. Here is exactly how to do it.
Single-cluster for one region, multi-cluster for global, or just a dev cluster? GPU nodes for training, CPU for serving? Scale requirements determine cluster architecture from day one.
EKS (AWS), GKE (Google), and AKS (Azure) each have quirks. Prefer engineers with experience on your cloud provider — they know the gotchas.
Generic Kubernetes experience is not enough for ML workloads. Ask specifically about GPU scheduling, Kubeflow, KServe, or Ray on Kubernetes if your workloads require them.
Kubernetes costs can spiral without proper resource requests, limits, and node autoscaling configuration. A good engineer sets up cost monitoring (Kubecost) before you hit a surprise bill.
Kubernetes Engineers on RemoteAI charge based on project scope and experience level. The following ranges reflect typical engagements for each type of project.
When you need to scale model serving based on traffic, run multiple AI services reliably, manage GPU resources efficiently, or achieve zero-downtime model updates. For small-scale or single-model serving, Docker Compose or a managed service is simpler.
Mid-level K8s engineers charge $80–$130/hr. Senior engineers with ML/MLOps Kubernetes experience charge $130–$220/hr.
Kubeflow is a Kubernetes-native ML platform for training, serving, and managing ML workflows. It runs training jobs, model serving, and experiment tracking — all on Kubernetes. It's complex to set up but powerful at scale.
GKE (Google) has the most mature Kubernetes implementation and best GPU support. EKS (AWS) integrates best with existing AWS services. AKS (Azure) is best if your stack is Microsoft-heavy. Choose based on where your other infrastructure lives.
Via the NVIDIA device plugin for Kubernetes, which exposes GPU resources as schedulable. Node pools with GPU instances (g4dn on AWS, a2 on GCP) are configured separately from CPU pools, and containers request GPU resources in their specs.