7 of 7 Kubeflow Jobs in London

Drive scalable lakehouse architectures (Delta Lake, Iceberg, Hudi) Cloud, Infrastructure & MLOps Champion multi-cloud engineering across Azure, AWS, and GCP Lead MLOps strategy (MLflow, Kubeflow, SageMaker, Vertex AI) and CI/CD pipelines Ensure scalable deployments using Kubernetes, Docker, and Infrastructure-as-Code Enterprise Integration (.NET Focus) Integrate AI capabilities ...

analytics workloads Cloud, Infrastructure & MLOps Champion multi-cloud engineering across Azure, AWS, and GCP within an enterprise-grade environment Lead MLOps strategy (MLflow, Kubeflow, SageMaker, Vertex AI) and industrialised CI/CD pipelines Ensure scalable, secure deployments using Kubernetes, Docker, and Infrastructure-as-Code Enterprise Integration (.NET Focus) Integrate ...

experience with core GenAI frameworks (eg, LangChain, LlamaIndex) and Agentic AI frameworks (eg, 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 ...

Machine Learning Engineer. Technical Skills: Strong in SQL and Python (Pandas, Scikit-learn, Jupyter, Matplotlib). Data transformation & manipulation: experience with Airflow, DBT and Kubeflow Cloud: Experience with GCP and Vertex AI (developing ML services). Expertise: Solid understanding of computer science fundamentals and time-series forecasting. Machine Learning: Strong ...

Machine Learning Engineer. Technical Skills: Strong in SQL and Python (Pandas, Scikit-learn, Jupyter, Matplotlib). Data transformation & manipulation : experience with Airflow, DBT and Kubeflow Cloud: Experience with GCP and Vertex AI (developing ML services). Expertise: Solid understanding of computer science fundamentals and time-series forecasting. Machine Learning: Strong ...

MLOps platforms, enabling ML and LLM workloads (not model research). Experience in areas such as: Building or operating MLOps platforms using tools like Kubeflow or similar frameworks Running model serving and inference platforms (e.g. KServe, vLLM, or equivalent) Supporting LLM-based workloads , including optimisation and serving considerations Providing notebook ...

need practical understanding of how ML and AI workloads behave in production. Experience or exposure to areas such as: MLOps platforms (e.g. Kubeflow or similar frameworks) Model serving and inference platforms (e.g. KServe, vLLM , or equivalent) Supporting LLM-based workloads , including performance and scaling considerations Notebook environments such as JupyterHub ...