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.
Our mission is to make sure nobody gets paged for a capacity problem we could have predicted. This role sits inside that mission directly: you'll buil...
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What happens when five specialized agents need to agree on one final answer? That's the coordination problem our retrieval and context-window teams ke...
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Our model registry and feature store already track what's been trained and what data it used. What's missing is a structured approval gate before a mo...
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Our clients include large-scale digital platforms and financial services companies processing millions of data subject requests per year under GDPR, C...
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Xivio Solutions manages GPU cluster infrastructure for enterprise ML teams, and one of the most common questions our clients ask is: which team, produ...
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Docker, Kubernetes, and container image registries are the foundation of what Covex Cloud manages for ML platform clients. We're building an AI-assist...
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Inovex Intelligence builds the ML developer platform that engineering teams use to ship AI features faster. One category of tooling we've been asked f...
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Mivex Cloud runs ML inference infrastructure for 60+ enterprise clients across healthcare, finance, and e-commerce. Our cluster scheduling stack is ma...
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Our clients deploy models in production and forget about them — until something goes wrong. Lumio Systems builds the monitoring layer that catches mod...
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Quelva Works builds workflow automation tools used by enterprise operations teams in insurance, financial services, and healthcare. In these industrie...
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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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