1 to 25 of 53 Kubeflow Jobs in England

with Generative AI and LLM deployment patterns. A proven history of reducing cloud spend on large-scale AI clusters. Experience with tools like MLflow, Kubeflow, LangSmith, or Phoenix. Expertise in AWS/GCP/Azure cost tools, Kubecost, or Cloudability. Extensive background of Kubernetes (K8s), Docker, and service meshes. Expert ...

with Generative AI and LLM deployment patterns. A proven history of reducing cloud spend on large-scale AI clusters. Experience with tools like MLflow, Kubeflow, LangSmith, or Phoenix. Expertise in AWS/GCP/Azure cost tools, Kubecost, or Cloudability. Extensive background of Kubernetes (K8s), Docker, and service meshes. Expert ...

cloud-native production environments. Demonstrated experience managing compute-intensive GPU infrastructure and high-performance computing (HPC) environments. Advanced proficiency in Kubernetes (K8s), Docker, Helm, KubeFlow, and service meshes (e.g., Istio). Hands-on experience with Terraform, Ansible, GitHub Actions, ArgoCD, or Jenkins. Experience with vLLM, Ray, MLflow, LangChain/LangSmith ...

Proven experience designing and deploying ML systems in production (5+ years in relevant roles) Proficiency in Python and SQL, and orchestration tools (Airflow, Kubeflow, Dagster, etc.) Experience with modern cloud platforms (preferably GCP or AWS), Kubernetes, and CI/CD workflows Understanding of ML model lifecycles: training, validation, deployment ...

cloud-native production environments. Demonstrated experience managing compute-intensive GPU infrastructure and high-performance computing (HPC) environments. Advanced proficiency in Kubernetes (K8s), Docker, Helm, KubeFlow, and service meshes (e.g., Istio).Hands-on experience with Terraform, Ansible, GitHub Actions, ArgoCD, or Jenkins. Experience with vLLM, Ray, MLflow, LangChain/LangSmith, DeepSpeed ...

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 ...

Studio, OpenAI, AKS), GCP (Vertex AI, Cloud Run), AWS (Bedrock, SageMaker)* Experience building and automating AI/ML pipelines using tools such as MLflow, Kubeflow, Azure ML, Vertex Pipelines, Airflow or Google ADK* Hands-on experience with Generative and Agentic AI frameworks such as LangChain, LlamaIndex, CrewAI, Autogen, Google ...

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 ...

/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 ...

teams. Desirableskills andexperience: Experience building platforms or applications that support Data Science, Machine Learning, or AI workloads. Experience with MLOps tooling such as MLflow, Kubeflow, Vertex AI, or similar technologies. Knowledge of workflow orchestration platforms such as Apache Airflow. Experience with data processing technologies such as Pandas, Apache Beam, Spark ...

Docker. Strong understanding of CI/CD principles and DevOps best practices. Experience supporting highly available, scalable production environments. Nice to have: Experience with Kubeflow and ML platform tooling. Exposure to AI, machine learning or GenAI projects. Experience with observability tooling such as Prometheus, Grafana or OpenTelemetry. Experience working within ...

related field. Preferred Qualifications Certifications such as: CISSP, CISM, CCSPCertified AI Security (e.g., CAISP or similar)Experience with: MLOps platforms (e.g., MLflow, Kubeflow)AI red teaming and adversarial testingKnowledge of secure coding and DevSecOps practices. Familiarity with Responsible AI principles and ethical AI frameworks. Key Skills AI/ML security ...

communicate technical information and ideas clearly at all levels and build trust with stakeholders Будет плюсом: designing or implementing DAG-based pipelines using Kubeflow, DVC, or Ray; big data technologies; constructing batch and streaming microservices exposed as REST/gRPC endpoints; container orchestration tools such as Kubernetes or Helm; open ...

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 Have constructed batch and streaming microservices exposed as REST/gRPC endpoints Experience with container orchestration tools (e.g. Kubernetes, Helm ...

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 ...

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 Have constructed batch and streaming microservices exposed as REST/gRPC endpoints Experience with container orchestration tools (e.g. Kubernetes, Helm ...

patterns. Agentic AI: Hands-on experience with AI Agents, Agent Development Kit (ADK), multi-agent frameworks, and RAG integration. MLOps/DevOps: Experience with Kubeflow, Docker, Kubernetes, and Linux environments. CI/CD: Proficiency in building and managing continuous integration and deployment pipelines. GenAI/LLM: Experience with LLMs, GenAI ...

ideas at all levels; convey information clearly and create trust with stakeholders. Preferred qualifications, capabilities, and skillsExperience designing/implementing pipelines using DAGs (e.g. Kubeflow, DVC, Ray)Experience of big data technologiesHave constructed batch and streaming microservices exposed as REST/gRPC endpointsExperience with container orchestration tools (e.g. Kubernetes, Helm ...

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 Have constructed batch and streaming microservices exposed as REST/gRPC endpoints Experience with container orchestration tools (e.g. Kubernetes, Helm ...

such as Vertex AI, BigQuery and Cloud Run. Hands-on experience with machine learning operations tooling and frameworks used with large datasets, such as Kubeflow Pipelines and dbt, including an understanding of production monitoring. Experience with Infrastructure as Code, including Terraform, Kubernetes, application programming interface authentication flows and networking. Experience ...

such as Vertex AI, BigQuery and Cloud Run. Hands-on experience with machine learning operations tooling and frameworks used with large datasets, such as Kubeflow Pipelines and dbt, including an understanding of production monitoring. Experience with Infrastructure as Code, including Terraform, Kubernetes, application programming interface authentication flows and networking. Experience ...

good grasp of IaC (Infrastructure-as-code) tools like Terraform or CloudFormation. Previous exposure to additional technologies like Seldon, KServe, RayServe, Kubernetes, MLFlow, Sagemaker, Kubeflow, Anyscale, Valohai, ArgoCD, Docker, Python, Java, Ray, Spark, Pandas, Argo Workflow, Postgres, Snowflake and BigQuery is highly desirable. Good to have: previous knowledge managing complementary ...

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 ...

reviews. Experience delivering AI or Machine Learning systems into production. Desirable experienceNVIDIA Jetson or other edge AI platforms. NVIDIA DeepStream, GStreamer or FFmpeg. Kubernetes, Kubeflow, ECS, Nomad or other container-orchestration platforms. Airflow, Prefect, Dagster, Metaflow or similar workflow tools. Kafka, Pulsar, Redis Streams, RabbitMQ or other event-driven technologies. ...

RayServe, Triton, vLLM, or similar) with real latency and cost constraints(preferred RayServe) MLOps pipeline tooling - training pipelines, model registries, feature stores, and lineage (Kubeflow, MLflow, Feast, Weights & Biases, or equivalents) LLMOps in production - inference serving, prompt/version management, and LLM observability (tracing, evals, drift, guardrails, cost per request ...