1 to 25 of 30 Permanent Kubeflow Jobs in London

. 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 ...

Ability to analyse large datasets and communicate insights effectively. Experience with LLMs, generative AI, and prompt engineering. Knowledge of MLOps tools and frameworks (MLflow, Kubeflow, Airflow).Familiarity with SQL, NoSQL, and distributed data technologies. Experience deploying AI models in production environments. Publications, patents, or open-source contributions in ML/ ...

/Langfuse, Gen AI/Agentic AI , Cloud platforms (Azure AI Foundry, AWS Bedrock/Sagemaker, GCP Vertex AI) , MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Preferred Delivered AI projects within Agile frameworks Experience on Gen AI Feedback Analysis, topic modelling, sentiment analysis Knowledge of AgentOps and OpenTelemetry ...

backend (Python, Node.js, Java), frontend (React, Angular), APIs (REST/GraphQL) Machine Learning: Foundations of MLflow, Weights & Biases, LoRA, PEFT, SageMaker, Vertex AI, AzureML, Kubeflow, Airflow, Prefect, TorchServe, Triton, vLLM, SHAP, LIME, pandas, Spark, SQLLLM tooling: Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context management Performance & observability ...

APIs, scripts and CI/CD automationWorking knowledge of modern MLOps stacks including experiment tracking and artifact managementHands-on experience with orchestration tools (e.g., Kubeflow, Apache Airflow, Metaflow or Prefect)Demonstrated skills with Docker, Kubernetes and distributed deploymentsPractical knowledge of Infrastructure-as-Code (Terraform) and a major cloud provider ...

Ability to analyse large datasets and communicate insights effectively. Experience with LLMs, generative AI, and prompt engineering. Knowledge of MLOps tools and frameworks (MLflow, Kubeflow, Airflow). Familiarity with SQL, NoSQL, and distributed data technologies. Experience deploying AI models in production environments. Publications, patents, or open-source contributions ...

Ability to analyse large datasets and communicate insights effectively. Experience with LLMs, generative AI, and prompt engineering. Knowledge of MLOps tools and frameworks (MLflow, Kubeflow, Airflow). Familiarity with SQL, NoSQL, and distributed data technologies. Experience deploying AI models in production environments. Publications, patents, or open-source contributions ...

Ability to analyse large datasets and communicate insights effectively. Experience with LLMs, generative AI, and prompt engineering. Knowledge of MLOps tools and frameworks (MLflow, Kubeflow, Airflow). Familiarity with SQL, NoSQL, and distributed data technologies. Experience deploying AI models in production environments. Publications, patents, or open-source contributions ...

environments. Collaborate with Data Scientists and Engineers to productionise models. Improve platform scalability, reliability, and automation. Key Skills Strong Python experience. MLOps tooling (MLflow, Kubeflow, SageMaker, or similar). Docker and Kubernetes. CI/CD tooling (GitHub Actions, GitLab CI, Azure DevOps). Cloud platforms (AWS, Azure, or GCP). ...

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 frameworksRunning model serving and inference platforms (e.g. KServe, vLLM, or equivalent)Supporting LLM‐based workloads, including optimisation and serving considerationsProviding notebook‐based environments such ...

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 considerationsNotebook environments such as JupyterHubAwareness ...

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 ...

systems or multi-modal perception.C++ development.CUDA and GPU optimisation.TensorRT, ONNX Runtime, OpenVINO or similar inference-optimisation technologies.NVIDIA Jetson or other edge-AI platforms.Docker, Kubernetes, Kubeflow or production MLOps pipelines.Distributed training or large-scale model optimisation.Synthetic-data generation, Unreal Engine or NVIDIA Omniverse.Aerial, satellite, drone or ISR imagery.Sensor fusion, autonomy ...

C++ development. CUDA and GPU optimisation. TensorRT, ONNX Runtime, OpenVINO or similar inference-optimisation technologies. NVIDIA Jetson or other edge-AI platforms. Docker, Kubernetes, Kubeflow or production MLOps pipelines. Distributed training or large-scale model optimisation. Synthetic-data generation, Unreal Engine or NVIDIA Omniverse. Aerial, satellite, drone or ISR imagery. ...

. 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 ...

tracking, segmentation or pose estimation.Multi-object localisation or multi-modal perception systems.GPU optimisation, CUDA, inference acceleration or edge AI.Real-time computer vision systems.Docker, Kubernetes, Kubeflow or MLOps pipelines.Aerial, satellite or ISR imagery.Synthetic data generation, Unreal Engine or NVIDIA Omniverse.Distributed training and large-scale model optimisation.Defence, government or national security projects.An ...

estimation. Multi-object localisation or multi-modal perception systems. GPU optimisation, CUDA, inference acceleration or edge AI. Real-time computer vision systems. Docker, Kubernetes, Kubeflow or MLOps pipelines. Aerial, satellite or ISR imagery. Synthetic data generation, Unreal Engine or NVIDIA Omniverse. Distributed training and large-scale model optimisation. Defence, government ...

Horovod, DeepSpeed, Megatron-LM, or equivalent multi-node training frameworks.Inference & serving: NVIDIA Triton, vLLM, TensorRT-LLM, or equivalent high-throughput serving platforms.MLOps/LLMOps: Kubeflow, MLflow, and at least one hyperscaler ML platform (SageMaker, Azure ML, or Vertex AI).Generative AI: LLM fine-tuning (LoRA/QLoRA), RAG architecture design … code: Terraform and Ansible for repeatable, automated provisioning of GPU clusters and AI platform environments; GitOps (ArgoCD) for continuous, declarative platform delivery.Pipeline orchestration: Kubeflow Pipelines, Apache Airflow, or Argo Workflows to orchestrate multi-stage training, fine-tuning, and inference pipelines.Cluster & workload scheduling: Slurm, Run:ai, and NVIDIA Base Command Manager ...

Megatron-LM, or equivalent multi-node training frameworks. Inference & serving: NVIDIA Triton, vLLM, TensorRT-LLM, or equivalent high-throughput serving platforms. MLOps/LLMOps: Kubeflow, MLflow, and at least one hyperscaler ML platform (SageMaker, Azure ML, or Vertex AI).Generative AI: LLM fine-tuning (LoRA/QLoRA), RAG architecture design … code: Terraform and Ansible for repeatable, automated provisioning of GPU clusters and AI platform environments; GitOps (ArgoCD) for continuous, declarative platform delivery. Pipeline orchestration: Kubeflow Pipelines, Apache Airflow, or Argo Workflows to orchestrate multi-stage training, fine-tuning, and inference pipelines. Cluster & workload scheduling: Slurm, Run:ai, and NVIDIA Base ...

infrastructure. Proficiency in programming languages such as Python, Java, or C++. Experience with data pipeline and workflow management tools (e.g., Apache Airflow, Kubeflow). Strong problem-solving skills and ability to work in a fast-paced environment. Excellent communication and collaboration skills. Proven experience in AI/ML infrastructure engineering ...

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 ...

infrastructure. Good proficiency in programming languages such as Python, Java, or C++. Experience with data pipeline and workflow management tools (e.g., Apache Airflow, Kubeflow). Strong problem-solving skills and ability to work in a fast-paced environment. Excellent communication and collaboration skills. Significant experience in AI/ML infrastructure ...

Prometheus, Grafana) Active SC clearance Nice to have Edge AI deployment, ideally NVIDIA Jetson and DeepStream Computer vision or video pipelines (e.g. GStreamer) Kubernetes, Kubeflow, Metaflow or similar orchestration tools Kafka or other event-driven architectures Data versioning tools (e.g. DVC, DeepLake) Experience with imagery, geospatial or sensor data Previous ...

architecture experience, including LLM hosting, RAG, vector databases, prompt orchestration and guardrails. Knowledge of AI platform tooling such as Azure ML, Amazon SageMaker, MLflow, Kubeflow and Databricks . Ability to evaluate technologies and make evidence-based architectural decisions. Experience designing secure, scalable and cost-optimised AI infrastructure. Strong architecture documentation ...