26 to 50 of 81 Kubeflow Jobs in London

Python, experience with AI/ML frameworks (e.g., TensorFlow, PyTorch), and experience with MLOps frameworks/tools (e.g., Sagemaker pipelines, Azure ML Studio, VertexAI, Kubeflow, MLFlow, Seldon, EvidentlyAI). #J-18808-Ljbffr More ❯

explainability and transparency in AI models. What You’ll Need: MLOps & Data Engineering experience – building scalable pipelines and automating workflows. Proficiency with tools like Kubeflow, MLflow, Airflow, Docker, Kubernetes, or similar . Experience in cloud environments (AWS, GCP, or Azure) for model deployment. Passion for AI in health tech and More ❯

NeurIPS, ICML, EMNLP, CVPR, etc.) and/or journals Strong high-level programming skills (e.g., Python), frameworks and tools such as DeepSpeed, Pytorch lightning, kubeflow, TensorFlow, etc. Strong written and verbal communication skills to operate in a cross functional team environment More ❯

reliability engineering. Strong expertise in multi-cloud and hybrid infrastructure including AWS, GCP, and on-premises environments. Experience with MLOps tooling such as MLFlow, Kubeflow, DataRobot, or similar platforms for ML lifecycle management. Experience with containerization and orchestration (Docker, Kubernetes) specifically for ML workloads and GPU clusters. Deep understanding of More ❯

days per week (right to work in the UK required). Nice‐to‐haves Clinical or health‐tech domain knowledge. MLOps tooling (MLflow, Kubeflow, Vertex Pipelines). Benefits Competitive salary and attractive equity in a high growth startup 25 days holiday + UK bank holidays. Flexible hours & focus on sustainable More ❯

LakeFS , or Databricks . Knowledge of security and compliance best practices (e.g., SOC2, ISO 27001). Exposure to MLOps platforms or frameworks (e.g., MLflow, Kubeflow, Vertex AI). What We Offer Competitive salary + equity Flexible work environment and remote-friendly culture Opportunities to work on cutting-edge AI/ More ❯

3days per week (right to work in the UK required). Nice‐to‐haves Clinical or health‐tech domain knowledge. MLOps tooling (MLflow, Kubeflow, Vertex Pipelines). Benefits Competitive salary and attractive equity in a high growth startup 25 days holiday +UK bank holidays. Flexible hours & focus on sustainable pace. More ❯

NeurIPS, ICML, EMNLP, CVPR, etc.) and/or journals. Strong high-level programming skills (e.g., Python), frameworks and tools such as DeepSpeed, Pytorch lightning, kubeflow, TensorFlow, etc. Strong written and verbal communication skills to operate in a cross functional team environment. About Us: At Scale, we believe that the transition More ❯

Familiarity with GPU acceleration using CUDA and model optimization for inference. Knowledge of MLOps tools for experiment tracking, and model serving such as MLflow, Kubeflow, or Weights & Biases. Excellent problem-solving skills and the ability to perform in a fast-paced, high-intensity environment. Cultural Fit - Intensity Required Ultralytics is More ❯

NeurIPS, ICML, EMNLP, CVPR, etc.) and/or journals Strong high-level programming skills (e.g., Python), frameworks and tools such as DeepSpeed, Pytorch lightning, kubeflow, TensorFlow, etc. Strong written and verbal communication skills to operate in a cross functional team environment PLEASE NOTE: Our policy requires a 90-day waiting More ❯

challenges into well-defined machine learning solutions We are using many technologies day to day such as various AWS services, GCP, Kubernetes, Ray Serve, Kubeflow, and ReTool. Any experience in these areas would be a bonus Sprout.ai Values Hungry for Growth - Unleash your inner Sprout: Sprouts embrace growth, forget comfort More ❯

Terraform You have experience with CI/CD pipelines, containerization technologies (e.g., Docker), and orchestration tools (e.g., Kubernetes) and using orchestration tools such as Kubeflow (our preferred tool) or similar frameworks like Apache Airflow to manage and automate ML workflows. You have experience with real-time data streaming technologies such More ❯

at all levels; convey information clearly and create trust with stakeholders. Preferred qualifications, capabilities, and skills Experience designing/implementing pipelines using DAGs (e.g. Kubeflow, DVC, Ray) Experience of big data technologies (e.g. Spark, Hadoop) Have constructed batch and streaming microservices exposed as REST/gRPC endpoints Familiarity with GraphQL More ❯

Terraform. You have experience with CI/CD pipelines, containerization technologies (e.g., Docker), and orchestration tools (e.g., Kubernetes) and using orchestration tools such as Kubeflow (our preferred tool) or similar frameworks like Apache Airflow to manage and automate ML workflows. You have experience with real-time data streaming technologies such More ❯

Terraform. You have experience with CI/CD pipelines, containerization technologies (e.g., Docker), and orchestration tools (e.g., Kubernetes) and using orchestration tools such as Kubeflow (our preferred tool) or similar frameworks like Apache Airflow to manage and automate ML workflows. You have experience with real-time data streaming technologies such More ❯

Familiarity with GPU acceleration using CUDA and model optimization for inference. Knowledge of MLOps tools for experiment tracking, and model serving such as MLflow, Kubeflow, or Weights & Biases. Excellent problem-solving skills and the ability to perform in a fast-paced, high-intensity environment. 🌟 Cultural Fit - Intensity Required Ultralytics is More ❯

Familiarity with GPU acceleration using CUDA and model optimization for inference. Knowledge of MLOps tools for experiment tracking, and model serving such as MLflow, Kubeflow, or Weights & Biases. Excellent problem-solving skills and the ability to perform in a fast-paced, high-intensity environment. 🌟 Cultural Fit - Intensity Required Ultralytics is More ❯

life cycle to enable the building and maintenance of our AI systems. Our teams make extensive use of open source technologies such as Kubernetes, Kubeflow, KServe, Argo, Buildpacks, and other cloud-native MLOps technologies. From technical governance to upstream collaboration, we are committed to enhancing the impact and sustainability of More ❯

life cycle to enable the building and maintenance of our AI systems. Our teams make extensive use of open source technologies such as, Kubernetes, Kubeflow, KServe, Argo, Buildpacks, and other cloud-native MLOps technologies. From technical governance to upstream collaboration, we are committed to enhancing the impact and sustainability of More ❯

practical handling of LLM context, prompt chaining, and prompt engineering. Hands-on experience with MLOps tools like MLflow, Weights & Biases, and orchestration platforms like Kubeflow or Databricks. Deep understanding of cloud platforms (especially Azure and AWS), containers, microservices, and event-driven architecture. Strong problem-solving and debugging skills, including debugging More ❯

hiring worldwide. What your day will look like Understand Ubuntu, Linux, networking and services in real-world environments Architect cloud infrastructure solutions like Kubernetes, Kubeflow, OpenStack, Ceph, and Spark either On-Premises or in Public Cloud (AWS, Azure, Google Cloud) Architect and integrate popular open source software such as PostgreSQL More ❯

engineers, and product managers. Nice-to-Haves: Experience with streaming platforms and understanding stream/table transformations. Familiarity with ML system deployment and management (Kubeflow, MLflow, Airflow, Flyte, etc.). Knowledge of monitoring, alerting, and operational best practices for data-intensive systems. Experience with Feature Stores or similar ML data More ❯

deep learning using TensorFlow. Experience implementing scalable, distributed, and highly available systems using Google Could Platform. Experience with Google AI Platform/Vertex AI, Kubeflow and Airflow. Proficient in Python. Java or Scala is a plus. Experience in data processing using SQL and PySpark. Experience working with foundation models and More ❯

Git) Desirables: Experience with cloud-based ML infrastructure, particularly GCP (Vertex AI, BigQuery), or equivalent (e.g. AWS, Azure) Exposure to orchestration tools such as Kubeflow pipelines or Airflow Familiarity with DBT or similar tools for modelling data in data warehouses Desire to build interpretable and explainable ML models (using techniques More ❯

Git) Desirables: Experience with cloud-based ML infrastructure, particularly GCP (Vertex AI, BigQuery), or equivalent (e.g. AWS, Azure) Exposure to orchestration tools such as Kubeflow pipelines or Airflow Familiarity with DBT or similar tools for modelling data in data warehouses Desire to build interpretable and explainable ML models (using techniques More ❯