51 to 65 of 65 Kubeflow Jobs in the UK

scale, including model lifecycle security, data pipelines, and secure deployment. Strong hands‐on experience with machine learning stacks, such as PyTorch, TensorFlow, MLflow, Kubeflow, or equivalent managed platforms (e.g. Vertex AI, Azure ML). Solid understanding of cloud‐native and containerised architectures, including Kubernetes and serverless technologies. Experience operating ...

clients Expertise in implementing SSO across a variety of industry standard software Preferred/Bonus Experience with MLOps/LLMOps (Softwares such as Sagemaker, Kubeflow or ZenML) Deployment of on-premise Kubernetes Prometheus (or other stacks) observability Experience with AWS Karpenter & Compute Optimizer Compliance literacy - ISO 27001, NIST SSDF/ ...

VertexAI TOOLS & TECHNOLOGIES Languages: Python, SQL Data & Transformation: dbt, Snowflake, BigQuery Visualisation & BI: Looker Engineering & MLOps: Docker, GitHub Workflow & Orchestration: Vertex AI Pipelines (GCP), Kubeflow LLMs & GenAI: Gemini API, Claude API INTERVIEW PROCESS Step 1: People Team Screening Call (30 min) Step 2: Hiring Manager Call: Experience (45 min) Step ...

required: AWS Kubernetes IaC (Terraform) GitOps (Helm, ArgoCD) Monitoring and alerting for production systems (Prometheus/Grafana or similar) Azure (desirable) MLOps in k8s (Kubeflow etc) Running GPU workloads on K8s (drivers, scheduling, utilisation) Familiarity with AI engineering Experience deploying in air-gapped customer environments Experience with secrets management ...

models - model versioning, experiment tracking, and deployment workflows. Work with data science teams to configure MLOps tooling such as Vertex AI, MLflow, Kubeflow, or similar platforms. Enable automated model retraining, drift detection, and performance monitoring pipelines. #J-18808-Ljbffr ...

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 We recognize ...

gapped deployment experience. Experience building self-serve ML platforms or tools for non-expert users. Experience with lakeFS , DVC , MLflow , Weights & Biases , Kubernetes, Kubeflow, Flyte, Dagster, Airflow, or Argo. LLM application, context engineering, structured output, or LLM evaluation experience. Exposure to radar, AIS, EO/IR fusion, tracking, sensor fusion ...

equivalent practical experience. Familiarity with our core stack, with strong experience in at least 2 components: Python, Typescript, Relational databases, APIs, Docker, Kubernetes, Kubeflow, AWS. Strong core software engineering and DevOps skills, including CI/CD, Version control, containerisation and cloud deployment. Knowledgeable on engineering best practices and comfortable with ...

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 technologies (e.g. Spark, Hadoop)Have constructed batch and streaming microservices exposed as REST/gRPC endpointsFamiliarity with GraphQL#CIBAppliedAIJ.P. Morgan ...

versed and proven experience 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. Longstanding experience in AI/ ...

tailored architectures for both Bare-Metal (Slurm, OpenMPI, bare-metal provisioning) and Cloud-Native/Kubernetes environments (NVIDIA GPU Operator, Network Operator, Run:ai, KubeFlow). Storage Integration: Architect high-bandwidth parallel storage solutions utilizing GPUDirect Storage (GDS) and enterprise AI file systems (e.g., VAST Data). Technical Sales Support ...

Define and evolve the enterprise MLOps architecture, enabling reproducible, automated, and governed AI model workflows. Lead teams in building and optimizing ML pipelines using Kubeflow Pipelines, Tekton, and Python SDKs. Architect scalable, production ready model serving solutions using KServe, Knative, and Triton (where applicable). Champion consistency in model registry … level security. In depth experience with GitOps at scale using ArgoCD, Helm, and automated cluster configuration patterns. Advanced knowledge of MLOps tooling (e.g., KServe, Kubeflow, Tekton, Knative) and ML workflow automation. Strong proficiency in Python, Bash, and automation frameworks like Ansible and Terraform. Deep experience with AWS, GCP, Azure ...

Learning to define and evolve Relation’s platform strategy. Our platform spans on-premises infrastructure and public cloud environments, supporting computational scientist notebooks through Kubeflow, model training and serving, internal services and genomic pipelines. You will set the technical direction across these areas, build and develop the team responsible … alongside experience operating infrastructure across hybrid on‐premises and cloud environments. Experience building, running or evolving MLOps infrastructure, ideally including notebook environments such as Kubeflow, GPU compute for distributed training and model serving. Familiarity with batch compute for data‐intensive scientific workloads, with an understanding of the reliability, scalability ...

solutions. Lead integration of compute, storage, networking, the AI software stack (CUDA, ROCm, Triton, NIM, NVIDIA AI Enterprise, Run:ai, Slurm, Kubernetes/Kubeflow) and managed-service operating models across multiple domains, delivery units and geographies. Build business cases, TCO and unit-economics models (cost per token, cost per training … knowledge of the AI software and orchestration stack: CUDA, cuDNN, NCCL, ROCm, Triton Inference Server, NIM, vLLM, TensorRT-LLM, Slurm, Kubernetes (with GPU Operator), Kubeflow, Run:ai, MLflow and NVIDIA AI Enterprise. Familiarity with datacenter facilities engineering for AI workloads: high-density power, liquid cooling (DLC, rear-door, immersion ...

solutions. Lead integration of compute, storage, networking, the AI software stack (CUDA, ROCm, Triton, NIM, NVIDIA AI Enterprise, Run:ai, Slurm, Kubernetes/Kubeflow) and managed-service operating models across multiple domains, delivery units and geographies. Build business cases, TCO and unit-economics models (cost per token, cost per training … knowledge of the AI software and orchestration stack: CUDA, cuDNN, NCCL, ROCm, Triton Inference Server, NIM, vLLM, TensorRT-LLM, Slurm, Kubernetes (with GPU Operator), Kubeflow, Run:ai, MLflow and NVIDIA AI Enterprise. Familiarity with datacenter facilities engineering for AI workloads: high-density power, liquid cooling (DLC, rear-door, immersion ...