1 to 25 of 44 Kubeflow Jobs in England

PyTorch, scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and orchestration. More ❯

PyTorch, scikit-learn, pandas, NumPy). Solid understanding of machine learning algorithms , statistical modelling , and deep learning architectures . Hands-on experience with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker, Vertex AI). Experience with data engineering concepts — ETL pipelines, data lakes, and cloud data platforms. Proficiency with cloud services (AWS, Azure, or GCP) for model deployment and orchestration. More ❯

pandas, numpy, sklearn, TensorFlow, PyTorch) Strong understanding and experience in implementing end-to-end ML pipelines (data, training, validation, serving) Experience with ML workflow orchestration tools (e.g., Airflow, Prefect, Kubeflow) and ML feature or data platforms (e.g., Tecton, Databricks, etc.) Experience with cloud platforms (AWS, GCP/Vertex, Azure), Docker, and Kubernetes Solid coding practices (Git, automated testing, CI/ More ❯

algorithms , NLP , deep learning , and statistical methods. Experience with Docker, Kubernetes , and cloud platforms like AWS/Azure/GCP . Hands-on experience with MLOps tools (MLflow, SageMaker, Kubeflow, etc.) and version control systems. Strong knowledge of APIs, microservices architecture, and CI/CD pipelines. Proven experience in leading teams, managing stakeholders, and delivering end-to-end AI/ More ❯

NumPy, Scikit-learn, TensorFlow, PyTorch). Hands-on experience implementing end-to-end ML pipelines (data ingestion, training, validation, serving). Familiarity with ML workflow orchestration tools (Airflow, Prefect, Kubeflow) and feature/data platforms (Databricks, Tecton, etc.). Strong experience with cloud platforms (AWS, GCP, or Azure), Docker, and Kubernetes. Solid coding practices, including Git, automated testing, and CI More ❯

NumPy, Scikit-learn, TensorFlow, PyTorch). Hands-on experience implementing end-to-end ML pipelines (data ingestion, training, validation, serving). Familiarity with ML workflow orchestration tools (Airflow, Prefect, Kubeflow) and feature/data platforms (Databricks, Tecton, etc.). Strong experience with cloud platforms (AWS, GCP, or Azure), Docker, and Kubernetes. Solid coding practices, including Git, automated testing, and CI More ❯

GCP, or Azure). Solid understanding of CI/CD pipelines and automated testing frameworks. Experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn. Familiarity with MLflow, Kubeflow, DVC, or similar MLOps tools . Understanding of cloud security principles , IAM, and networking best practices. Proficiency in Python and Bash scripting for automation and tooling development. Version control with More ❯

processes, ensuring reproducibility and auditability. Implementing monitoring and observability to detect drift, bias, and performance degradation, and setting up rollback/recovery processes. Using MLOps tools (e.g., Vertex Pipelines, Kubeflow, Weights & Biases) for experiment tracking, model registry, and automated deployment. Leveraging Docker/Kubernetes and workflow orchestration tools (Airflow, Prefect, Dagster). Collaborating with product, design, and engineering teams to More ❯

in applied data science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). • Proven More ❯

in applied data science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). • Proven More ❯

in applied data science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). • Proven More ❯

production environments serving real users. Strong proficiency in containerisation (Docker, Kubernetes) and orchestration of multi-stage ML workflows. Hands-on experience with ML platforms and tools such as MLflow, Kubeflow, Vertex AI, SageMaker, or similar model management systems. Practical knowledge of infrastructure as code, CI/CD best practices, and cloud platforms (AWS, GCP, or Azure). Experience with relational More ❯

production environments serving real users. Strong proficiency in containerisation (Docker, Kubernetes) and orchestration of multi-stage ML workflows. Hands-on experience with ML platforms and tools such as MLflow, Kubeflow, Vertex AI, SageMaker, or similar model management systems. Practical knowledge of infrastructure as code, CI/CD best practices, and cloud platforms (AWS, GCP, or Azure). Experience with relational More ❯

monitoring, logging, and performance testing for GPU/ML workloads. Excellent collaboration skills — able to work with research, engineering, and product teams. Desirables: Experience in MLOps/LLMOps (MLflow, Kubeflow, Weights & Biases, experiment tracking). Exposure to video processing, compression, or neural rendering pipelines. Knowledge of FPGA/embedded deployment Contributions to open-source GPU/ML/DevOps projects. More ❯

and optimise CI/CD pipelines to automate the deployment of AI applications and models (MLOps/LLMOps). Containerise workloads using Docker and manage deployments through Kubernetes (KubeRay, Kubeflow, MLRun, or similar). Provision and manage the infrastructure required to run AI workloads, including vector databases and hyperscaler AI services. Develop automation scripts in Python to streamline operations and More ❯

and optimise CI/CD pipelines to automate the deployment of AI applications and models (MLOps/LLMOps). Containerise workloads using Docker and manage deployments through Kubernetes (KubeRay, Kubeflow, MLRun, or similar). Provision and manage the infrastructure required to run AI workloads, including vector databases and hyperscaler AI services. Develop automation scripts in Python to streamline operations and More ❯

systems. Key Responsibilities Build model lifecycle tooling (deployment, monitoring and alerting) for our predictive models (for example claims cost, conversion, retention, market models) Maintain and improve the development environment (Kubeflow) used by our Data Scientists and Actuaries Develop and maintain pricing analytics tools that enable faster impact assessments, reducing manual work Collaborate with the technical pricing, street pricing and product More ❯

engineering principles for building scalable, maintainable, and production-ready AI systems. Proficiency in Python with AI/ML frameworks (PyTorch, TensorFlow). Experience with MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Deep proficiency in Python and extensive experience with relevant AI/ML/NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK). Proven experience developing applications leveraging More ❯

engineering principles for building scalable, maintainable, and production-ready AI systems. Proficiency in Python with AI/ML frameworks (PyTorch, TensorFlow). Experience with MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Deep proficiency in Python and extensive experience with relevant AI/ML/NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK). Proven experience developing applications leveraging More ❯

in applied data science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). • Proven More ❯

in applied data science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). • Proven More ❯

in applied data science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). • Proven More ❯

rigorous code quality and testing standards across data science projects Support talent acquisition and continuous learning initiatives Knowledge and Experience Knowledge of ML model development and deployment frameworks (MLFlow, Kubeflow Advanced data querying (SQL) and data engineering pipelines (Airflow Extensive experience with comprehensive unit testing, integration testing, and test coverage strategies Experience working with Product Management teams and ability to More ❯

tuning, and evaluation. Hands-on experience with core GenAI frameworks (e.g., LangChain, LlamaIndex) and Agentic AI frameworks (e.g., AutoGen, CrewAI, LangGraph). Proficiency in MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Strong working knowledge and practical deployment experience on major cloud platforms (AWS, Azure, GCP), including their AI/ML services. Strong foundation in software engineering principles for More ❯

tuning, and evaluation. Hands-on experience with core GenAI frameworks (e.g., LangChain, LlamaIndex) and Agentic AI frameworks (e.g., AutoGen, CrewAI, LangGraph). Proficiency in MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Strong working knowledge and practical deployment experience on major cloud platforms (AWS, Azure, GCP), including their AI/ML services. Strong foundation in software engineering principles for More ❯