7 of 7 Remote/Hybrid Kubeflow Jobs in the UK

production environments. Knowledge of distributed training, GPU/TPU optimization, and cloud platforms (AWS, GCP, Azure). Familiarity with MLOps tools like MLflow, Kubeflow, or Vertex AI for scalable ML pipelines. Excellent leadership, communication, and cross-functional collaboration skills. Bachelor’s or Master’s in Computer Science, Engineering, or related ...

with GitHub Actions and/or Argo CD. Preferred/Bonus Research experience in edge AI or constrained/offline deployments. MLOps experience (Sagemaker, Kubeflow, ZenML). Experience building RESTful services around AI pipelines. ISO 27001, NIST SSDF, OWASP SAMM, or GDPR compliance literacy. Experience with AWS Karpenter, Prometheus ...

working knowledge of: Cloud infrastructure, including Kubernetes, OpenStack, Docker, microservices, and public cloud services (AWS, GCP, Azure) AI/ML infrastructure tools (e.g., Kubeflow, MLflow, NVIDIA AI), and how they integrate with cloud-native platforms DevOps and automation tools, including CI/CD pipelines Observability tooling, including open-source monitoring ...

working knowledge of: Cloud infrastructure, including Kubernetes, OpenStack, Docker, microservices, and public cloud services (AWS, GCP, Azure) AI/ML infrastructure tools (e.g., Kubeflow, MLflow, NVIDIA AI), and how they integrate with cloud-native platforms DevOps and automation tools, including CI/CD pipelines Observability tooling, including open-source monitoring ...

working knowledge of: Cloud infrastructure, including Kubernetes, OpenStack, Docker, microservices, and public cloud services (AWS, GCP, Azure) AI/ML infrastructure tools (e.g., Kubeflow, MLflow, NVIDIA AI), and how they integrate with cloud-native platforms DevOps and automation tools, including CI/CD pipelines Observability tooling, including open-source monitoring ...

working knowledge of: Cloud infrastructure, including Kubernetes, OpenStack, Docker, microservices, and public cloud services (AWS, GCP, Azure) AI/ML infrastructure tools (e.g., Kubeflow, MLflow, NVIDIA AI), and how they integrate with cloud-native platforms DevOps and automation tools, including CI/CD pipelines Observability tooling, including open-source monitoring ...

working knowledge of: Cloud infrastructure, including Kubernetes, OpenStack, Docker, microservices, and public cloud services (AWS, GCP, Azure) AI/ML infrastructure tools (e.g., Kubeflow, MLflow, NVIDIA AI), and how they integrate with cloud-native platforms DevOps and automation tools, including CI/CD pipelines Observability tooling, including open-source monitoring ...