11 of 11 Permanent Kubeflow Jobs in England

Azure) and containerisation (Docker, Kubernetes) Solid understanding of ML fundamentals (model training, evaluation, common architectures) Desired Skills (Bonus Points) Experience with MLOps tooling (MLflow, Kubeflow, Weights & Biases, or similar) Building data pipelines (real-time or batch) using tools like Apache Spark, Kafka, Airflow, or dbt Familiarity with recommender systems, transformers ...

lifecycle management Lead documentation, knowledge transfer, and mentoring to upskill internal teams Technical requirements Strong Python and ML frameworks (PyTorch, TensorFlow) MLOps tooling (MLflow, Kubeflow or similar) Databricks Docker, Kubernetes, CI/CD pipelines Cloud platforms (AWS, Azure or GCP); Terraform experience preferred Financial services experience highly desirable A hands ...

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 ...

Milvus) Knowledge graphs and semantic search Data engineering pipelines MLOps & Deployment CI/CD for ML, model monitoring, versioning Kubernetes, Docker, serverless architectures MLflow, Kubeflow, SageMaker or similar Cloud & Architecture Azure AI, AWS AI/Bedrock, Google Vertex AI API-led architectures and microservices Security, governance, and responsible AI Frameworks ...

advantageous). Strong grasp of CI/CD, testing, version control, secure coding, and modern engineering workflows. Exposure to MLOps tools such as MLflow, Kubeflow, or Azure ML. ...

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 ...

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 ...