7 of 7 Permanent MLflow Jobs in the Thames Valley

relevant academic (research Masters level) and/or industry experience. Excellent knowledge and experience of managing an on-premise Kubenetes cluster. Excellent knowledge of Kubeflow and similar systems, e.g. MLflow Good programming ability in Python with familiarity with Linux systems including scripting and system configuration. Experience using AWS, e.g, Cognito, S3, EC2, Lamdas, etc. Experience with ML toolkits, e.g. PyTorch More ❯

Experience Academic background (research Masters level) or industry experience in a relevant field Strong experience managing on premise Kubernetes clusters Deep knowledge of Kubeflow or similar systems such as MLflow Proficient in Python and experienced with Linux systems Familiar with AWS services such as Cognito, S3, EC2 and Lambda Experience working with ML frameworks such as PyTorch or Lightning Capable More ❯

Experience Academic background (research Masters level) or industry experience in a relevant field Strong experience managing on premise Kubernetes clusters Deep knowledge of Kubeflow or similar systems such as MLflow Proficient in Python and experienced with Linux systems Familiar with AWS services such as Cognito, S3, EC2 and Lambda Experience working with ML frameworks such as PyTorch or Lightning Capable More ❯

call expectation outside of core office hours . Key Responsibilities: Manage and maintain on-premise Kubernetes clusters Implement and maintain ML Ops pipelines using Kubeflow and similar tools (e.g., MLflow) Develop scripts and tooling in Python; manage Linux system configurations Leverage AWS infrastructure (Cognito, S3, EC2, Lambda, etc.) Integrate ML toolkits (e.g., PyTorch, Lightning) into ML Ops workflows Design and More ❯

call expectation outside of core office hours . Key Responsibilities: Manage and maintain on-premise Kubernetes clusters Implement and maintain ML Ops pipelines using Kubeflow and similar tools (e.g., MLflow) Develop scripts and tooling in Python; manage Linux system configurations Leverage AWS infrastructure (Cognito, S3, EC2, Lambda, etc.) Integrate ML toolkits (e.g., PyTorch, Lightning) into ML Ops workflows Design and More ❯

research Masters level) and/or industry experience. Essential Skills Excellent knowledge and experience of managing an on-premise Kubenetes cluster. Excellent knowledge of Kubeflow and similar systems, e.g. MLflow Good programming ability in Python with familiarity with Linux systems including scripting and system configuration. Experience using AWS, e.g, Cognito, S3, EC2, Lamdas, etc. Experience with ML toolkits, e.g. PyTorch More ❯

both on-prem, self-managed systems and also leverage AWS infrastructure. Essential: Experience of managing an on-prem Kubernetes clusters. Working knowledge of Kubeflow and other systems such as MLflow Exposure to cloud based systems e.g. AWS, Azure or GCP (they use AWS) Knowledge of Linux systems including scripting and system configuration. Understanding of how ML tools fit into MLOps More ❯