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Kubernetes is the operating system of modern AI infrastructure — but running ML workloads on it is meaningfully harder than running web services. GPU scheduling, multi-tenant resource isolation, distributed training job management, and model serving at variable load all introduce complexity that standard Kubernetes experience does not cover. Post your Kubernetes job on RemoteAI and hire an engineer who has run GPU workloads on Kubernetes in production.
Pre-vetted talent pool
These are the types of Kubernetes engineers available on RemoteAI — all pre-vetted, confirmed profiles.
Cloud provider (EKS, GKE, AKS, on-premise), whether GPU scheduling is needed, the ML serving or training use case, and the current pain point — scaling, cost, reliability, or a new platform build.
GPU scheduling, model serving operators, and distributed training job management — not just engineers who have deployed web apps on Kubernetes.
Ask for real examples of GPU node pool setup, model serving deployments, or distributed training job configs they have run in production.
RemoteAI takes nothing from your contract.
Hiring a kubernetes engineer remotely starts with a clear, well-scoped job post. This guide covers what to include, how RemoteAI's matching works, and what you can expect at each step of the process.
Kubernetes engineers on RemoteAI who work on ML infrastructure handle the full platform layer: configuring GPU node pools and the NVIDIA device plugin for AI workload scheduling, deploying and operating model serving platforms (KServe, Triton Inference Server, vLLM) on Kubernetes, managing distributed training jobs using Kubeflow Training Operator or PyTorch Operator, implementing horizontal pod autoscaling and custom metrics scaling for inference load, and designing multi-tenant cluster architectures with namespace isolation and resource quotas. They also handle operational details: cluster upgrades with zero ML downtime, persistent volume configuration for large model weights, and network policies for secure multi-tenant environments.
A well-structured job post attracts the right kubernetes engineers and filters out irrelevant applications. Here is the exact process to follow on RemoteAI.
Cloud provider (EKS, GKE, AKS, on-premise), whether GPU scheduling is needed, the ML serving or training use case, and the current pain point — scaling, cost, reliability, or a new platform build.
GPU scheduling, model serving operators, and distributed training job management — not just engineers who have deployed web apps on Kubernetes.
Ask for real examples of GPU node pool setup, model serving deployments, or distributed training job configs they have run in production.
RemoteAI takes nothing from your contract.
Looking to browse talent directly?
View vetted Kubernetes Engineer Jobs and contact them directly — no job post needed.
Cloud provider, GPU requirements, whether ML serving (KServe, Triton, vLLM) or training orchestration (Kubeflow) is involved, cluster scale, and whether this is platform setup from scratch or optimization of an existing cluster.
Managed ML platforms abstract away cluster management but lock you into a specific cloud and pricing model. Kubernetes gives full control and portability. For teams with significant ML infrastructure needs and the engineers to manage it, Kubernetes plus KServe is typically cheaper and more flexible at scale. For teams that want zero ops overhead, managed platforms are the right trade-off.
Free. No listing fee, no commission on contracts.
Kubernetes engineers on RemoteAI typically charge $90–$200/hour. GPU scheduling and ML platform specialists are at the higher end.
Sign up free and describe the cloud provider, GPU requirements, and ML workload type. RemoteAI surfaces Kubernetes engineers with relevant ML infrastructure experience. Most posts have a shortlist within 24 hours.