1 to 25 of 50 Kubeflow Jobs in the UK

versions of Java and/or Python programming languages Experience with MLOps tools and platforms (e.g., MLflow, Amazon SageMaker, Google VertexAI, Databricks, BentoML, KServe, Kubeflow) Experience with cloud technologies (AWS/Azure/GCP) and distributed systems, web technologies and event drive architectures Understanding of data versioning and ML models ...

role at present Desirable (Nice to Have) Experience with streaming and big‐data tooling – Apache Kafka, Spark, or Flink Experience with MLOps platforms – MLflow, Kubeflow, Vertex AI, SageMaker, or Weights & Biases Experience evaluating and optimising RAG systems at scale (retrieval quality, chunking, re‐ranking) Experience designing multi‐agent systems ...

Milvus, pgvector, or similar retrieval platforms. Hands‐on use of LangGraph, OpenAI Agents SDK, Google ADK, CrewAI, or other agentic frameworks. Familiarity with MLflow, Kubeflow, Ragas, DeepEval, OpenJudge, LangSmith, or comparable MLOps/LLMOps and evaluation tooling. Experience with vLLM, NVIDIA Triton, quantization, GPU memory sizing, or distributed inference. Python ...

Milvus, pgvector, or similar retrieval platforms. Hands‐on use of LangGraph, OpenAI Agents SDK, Google ADK, CrewAI, or other agentic frameworks. Familiarity with MLflow, Kubeflow, Ragas, DeepEval, OpenJudge, LangSmith, or comparable MLOps/LLMOps and evaluation tooling. Experience with vLLM, NVIDIA Triton, quantization, GPU memory sizing, or distributed inference. Python ...

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 ...

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, SQL LLM tooling: Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context management Performance ...

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, SQL LLM tooling: Familiarity with LangChain, Semantic Kernel, prompt chaining, vector search, and context management Performance ...

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 ...

skills and attention to detail Experience with LLMs and generative AI (OpenAI, Hugging Face, LangChain) Knowledge of NLP or computer vision Experience with MLflow, Kubeflow or similar MLOps tools What We Offer ✓ Competitive salary based on experience ✓ Diverse AI projects across multiple industries ✓ Collaborative and research-driven team environment ...

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 ...

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 ...

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 (eg KServe, vLLM, or equivalent) Supporting LLM-based workloads , including optimisation and serving considerations Providing notebook ...

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 ...

domain. Strong experience with ML infrastructure — you've deployed and operated models in production. Comfortable with Python and the modern ML tooling ecosystem (MLflow, Kubeflow, Airflow, or similar). Deep understanding of containerization and orchestration (Docker, Kubernetes). Experience with cloud platforms (AWS, GCP, or Azure) and infrastructure-as-code. ...

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 of emerging ...

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 (eg Kubeflow or similar frameworks) Model serving and inference platforms (eg KServe, vLLM , or equivalent) Supporting LLM-based workloads , including performance and scaling considerations Notebook environments such as JupyterHub ...

automated evaluation. Education: PhD in AI/ML/CS or a related field. Beneficial knowledge: Distributed model training & infra: PyTorch DDP and FSDP, Kubeflow, Slurm, Kubernetes, and a large‐scale training framework such as DeepSpeed, Megatron or TorchTitan. LLM/Agent tooling: evaluation harnesses, prompt optimisation, agent frameworks. Inference ...

training, deployment and inference. Improve developer experience through automation, reusable tooling and self-service capabilities. Support orchestration and distributed workloads using technologies such as Kubeflow and Ray. Establish reliable CI/CD workflows, observability and deployment processes. Balance engineering productivity against the security, resilience and governance requirements of a regulated ...

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 ...

including real-time serving, feature stores, autoscaling, and blue/green deployments Strong Python and SQL skills Hands-on GCP experience, especially Vertex AI, Kubeflow pipelines, and BigQuery — or strong fluency on an equivalent cloud with a clear ability to get to Vertex fast Solid CI/CD and production ...

environments. Python: Strong capability in developing production-quality AI/ML code. LLMOps & AI Model Management: Familiarity with tools like MLflow, LangChain, Hugging Face, Kubeflow, or similar platforms. Data Processing: Working knowledge of Databricks/Spark or comparable large-scale data processing tools. SQL: Solid capabilities in data querying ...

knowledge systems at scale Experience designing evaluation frameworks for AI-generated or retrieval-based outputs Exposure to MLOps tools and practices (e.g., MLflow, Kubeflow, SageMaker) Experience optimizing systems for performance, scalability, or cost Familiarity with frameworks or tools for orchestrating LLM-based workflows and agent-like systems Experience working ...

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 … 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 ...