Remote positions
Browse vetted remote Kubernetes positions — AI infrastructure, MLOps platforms, GPU cluster orchestration, and model serving at scale.
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Full-time roles and fixed-scope freelance missions — matched to your AI skills.
Posted 8 months ago
We have 14 ML models in production and feature engineering is duplicated everywhere. Each model team maintains their own pipelines. When one team fixe...
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Posted 8 months ago
We serve 10M+ ML predictions per day and our on-call rotation is a disaster. Right now when something goes wrong at 2am, the engineer on call has no r...
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Posted 9 months ago
We need an experienced MLOps engineer for a 6-month engagement to help us migrate our ML model serving from a manual notebook-based workflow to a full...
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Role guide
Kubernetes is the standard infrastructure platform for production AI systems at scale. Remote Kubernetes jobs at AI companies involve GPU node pool management, MLOps platform engineering, and autoscaling model serving. These roles sit at the intersection of cloud infrastructure and ML systems.
What Kubernetes roles are available remotely in AI?
Primarily MLOps platform engineering, AI infrastructure, and model serving roles. These positions require strong Kubernetes fundamentals plus ML-specific tooling knowledge (Kubeflow, KServe, GPU scheduling).
Do remote Kubernetes AI jobs require cloud provider experience?
Almost always. EKS (AWS), GKE (Google Cloud), and AKS (Azure) are the most common managed Kubernetes platforms. Most job listings specify a primary cloud provider.
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