5 of 5 Kubeflow Jobs in London

learn Eagerness to learn, curiosity, and a problem-solving mindset Nice to Have Cloud experience (AWS, GCP, Azure) Familiarity with MLOps tools (Docker, MLflow, Kubeflow) Deep learning knowledge (CNNs, RNNs, Transformers) Internship, research, or academic projects in ML/AI Why Join Hands-on experience with cutting-edge AI technologies ...

Unit Testing). Familiarity with C++ or Go is a significant plus. Tooling & Orchestration: Deep experience with containerization (Docker, Kubernetes) and orchestration tools like Kubeflow, Airflow, or Metaflow. Cloud Architecture: Expertise in deploying ML workloads on AWS, GCP, or Azure, utilizing managed services like SageMaker or Vertex AI. Data Foundations ...

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 ...

data services (e.g., Vertex AI, Dataflow, BigQuery, AI Platform, Pub/Sub). Hands‐on experience with ML training frameworks such as TFX or Kubeflow Pipelines, and model‐serving technologies like TensorFlow Serving , Triton, or TorchServe. Background working with large‐scale batch and real‐time data processing systems. Strong understanding ...