6 of 6 Permanent Kubeflow Jobs in the Thames Valley

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 ...

Azure) Nice to Have Experience with fraud, risk, credit, AML, pricing, or customer analytics use cases Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, etc.) Docker and Kubernetes experience Exposure to model governance, explainability, or regulatory frameworks Contract Details £650–£750 per day (Outside IR35) Initial 6-month contract, with strong ...

Azure) Nice to Have Experience with fraud, risk, credit, AML, pricing, or customer analytics use cases Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, etc.) Docker and Kubernetes experience Exposure to model governance, explainability, or regulatory frameworks Contract Details £650–£750 per day (Outside IR35) Initial 6-month contract, with strong ...

Azure) Nice to Have Experience with fraud, risk, credit, AML, pricing, or customer analytics use cases Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, etc.) Docker and Kubernetes experience Exposure to model governance, explainability, or regulatory frameworks Contract Details £650–£750 per day (Outside IR35) Initial 6-month contract, with strong ...

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 ...

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 ...