1 to 25 of 32 Kubeflow Jobs in the UK

with advertising data Experience with FastAPI or similar Python API frameworks Proficiency with dashboarding tools such as Looker Studio, Tableau, or PowerBI Experience working on SaaS applications Knowledge of Kubeflow Pipelines Experience developing on Mac/Unix environments Understanding of CICD pipeline design and implementation Experience with GenAI, LLMs, or neural networks A/B testing or statistical hypothesis testing More ❯

algorithms , NLP , deep learning , and statistical methods. Experience with Docker, Kubernetes , and cloud platforms like AWS/Azure/GCP . Hands-on experience with MLOps tools (MLflow, SageMaker, Kubeflow, etc.) and version control systems. Strong knowledge of APIs, microservices architecture, and CI/CD pipelines. Proven experience in leading teams, managing stakeholders, and delivering end-to-end AI/ More ❯

or other Python API frameworks * Experience with dashboarding tools such as Looker Studio, Tableau or PowerBI * Experience working on a SaaS (Software-as-a-Service) application * Experience working with Kubeflow Pipelines * Experience developing on a Mac/Unix environment * Knowledge of CICD pipeline design & implementation * Experience modelling using GenAI, LLMs or neural networks in general * A/B testing experience More ❯

Python and ML/DL libraries (NumPy, Pandas, scikit-learn). Proven track record of building and deploying ML models in production environments. Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes, or similar). Experience working with cloud platforms (AWS, Azure, or GCP). Solid understanding of CNNs, object detection, segmentation, and image classification. Strong problem-solving skills and More ❯

Python and ML/DL libraries (NumPy, Pandas, scikit-learn). Proven track record of building and deploying ML models in production environments. Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes, or similar). Experience working with cloud platforms (AWS, Azure, or GCP). Solid understanding of CNNs, object detection, segmentation, and image classification. Strong problem-solving skills and More ❯

Python and ML/DL libraries (NumPy, Pandas, scikit-learn). Proven track record of building and deploying ML models in production environments. Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes, or similar). Experience working with cloud platforms (AWS, Azure, or GCP). Solid understanding of CNNs, object detection, segmentation, and image classification. Strong problem-solving skills and More ❯

Python and ML/DL libraries (NumPy, Pandas, scikit-learn). Proven track record of building and deploying ML models in production environments. Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes, or similar). Experience working with cloud platforms (AWS, Azure, or GCP). Solid understanding of CNNs, object detection, segmentation, and image classification. Strong problem-solving skills and More ❯

Python and ML/DL libraries (NumPy, Pandas, scikit-learn). Proven track record of building and deploying ML models in production environments. Knowledge of MLOps tools (e.g., MLflow, Kubeflow, Docker, Kubernetes, or similar). Experience working with cloud platforms (AWS, Azure, or GCP). Solid understanding of CNNs, object detection, segmentation, and image classification. Strong problem-solving skills and More ❯

experience. Nice to have skills Experience with Machine Learning (ML): deploying and managing models, creating inference pipelines, and ML Ops practices. Knowledge of ML platforms such as Airflow, SageMaker, Kubeflow, or MLFlow. Experience with distributed computing (e.g., Spark/PySpark). Understanding of cloud ML deployment and model serving on platforms like AWS, Azure, or GCP. Experience with Large Language More ❯

AI skills - experience with large language models (LLMs) such as GPT-4/5, fine-tuning, RAG pipelines, or AI-powered chatbots. MLOps and integration - experience with frameworks like Kubeflow, MLflow, or Vertex AI, and deploying models behind APIs with monitoring and feedback loops. AI guardrails & quality - designing safeguards, judge models, automated evaluation, or compliance mechanisms for production AI systems. More ❯

Docker, Kubernetes) and frameworks like BentoML or equivalent. Familiarity with vector databases and retrieval pipelines for RAG architectures. Knowledge of cloud platforms (AWS, GCP, Azure) and MLOps tooling (MLflow, Kubeflow, or similar). Familiarity with voice-to-text, IVR, and/or computer vision systems is a plus. Strong understanding of software engineering best practices-testing, CI/CD, version More ❯

systems. Key Responsibilities Build model lifecycle tooling (deployment, monitoring and alerting) for our predictive models (for example claims cost, conversion, retention, market models) Maintain and improve the development environment (Kubeflow) used by our Data Scientists and Actuaries Develop and maintain pricing analytics tools that enable faster impact assessments, reducing manual work Collaborate with the technical pricing, street pricing and product More ❯

systems. Key Responsibilities Build model lifecycle tooling (deployment, monitoring and alerting) for our predictive models (for example claims cost, conversion, retention, market models) Maintain and improve the development environment (Kubeflow) used by our Data Scientists and Actuaries Develop and maintain pricing analytics tools that enable faster impact assessments, reducing manual work Collaborate with the technical pricing, street pricing and product More ❯

and the ability to influence A drive to learn new technologies and techniques Experience/aptitude towards research and openness to learn new technologies Experience with Azure, Spark (PySpark) Kubeflow - desirable We pay competitive salaries based on experience of the candidates, along with this, you will be entitled to an award-winning range of benefits including: Access to our company More ❯

with a strong grounding in evaluating NLP models using classification and ranking metrics, and experience running A/B or offline benchmarks. Proficient with MLOps and training infrastructure (MLflow, Kubeflow, Airflow), including CI/CD, hyperparameter tuning, and model versioning. Strong social media data extraction and scraping skills at scale (Twitter v2, Reddit, Discord, Telegram, Scrapy, Playwright). Experience with More ❯

Knowledge Experience taking models from experiments through to production deployments, with tools such as Docker, Kubernetes & serverless alternatives such as AWS Lambda. Familiarity with MLOps tools such as MLFlow, Kubeflow or Sagemaker. A strong knowledge of cloud platforms (ideally AWS) and their respective services for deploying robust, AI-heavy applications. Bonus Experience Experience with named entity recognition/recommendation systems. More ❯

Knowledge of cutting-edge techniques for Natural Language Processing and Computer Vision Strong grasp of basic probability concepts and machine learning lifecycle Experience with workflow and pipelining frameworks (e.g., Kubeflow, MLFlow, Argo) Understanding and application of Ethical AI considerations Ready to take your career to the next level? Apply today and be part of something extraordinary! Please either apply by More ❯

Knowledge of cutting-edge techniques for Natural Language Processing and Computer Vision Strong grasp of basic probability concepts and machine learning lifecycle Experience with workflow and pipelining frameworks (e.g., Kubeflow, MLFlow, Argo) Understanding and application of Ethical AI considerations Ready to take your career to the next level? Apply today and be part of something extraordinary! Please either apply by More ❯

and PyTorch Knowledge of Natural Language Processing (NLP) and Computer Vision techniques Strong understanding of probability concepts and the machine learning lifecycle Experience with workflow and pipelining frameworks (e.g., Kubeflow, MLFlow, Argo) Awareness and application of Ethical AI principles This role offers a unique opportunity to work on cutting-edge projects, expand your expertise, and contribute to innovative AI and More ❯

with version controls systems (e.g. 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 such as SHAP) Desire to More ❯

with version controls systems (e.g. 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 such as SHAP) Desire to More ❯

with version controls systems (e.g. 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 such as SHAP) Desire to More ❯

and scripting for automation Solid understanding of cloud networking, security, and cross-cloud connectivity Experience in monitoring, observability, and cost optimisation Nice to Have Experience with ML tooling (MLflow, Kubeflow) Knowledge of FastAPI , Databricks, or Snowflake Exposure to SRE practices or cloud security certifications Familiarity with Prometheus , Grafana , or Datadog Interested? If you want to be part of a world More ❯

platform engineering teams to identify technical gaps, scope backend development efforts, and deliver core platform capabilities such as: Model training orchestration CI/CD for ML (e.g., using MLflow, Kubeflow, or Vertex AI Pipelines) Model versioning, monitoring, and governance Enable high-impact AdTech use cases including: Marketing Mix Modeling (MMM) Real-time personalization and bidding Audience segmentation and targeting Predictive More ❯

scalable training pipelines for large datasets. Working experience leading complex, cross-functional projects and influencing technical direction across multiple teams. Familiarity with modern workflow orchestration tools such as Prefect, Kubeflow, Argo, etc. Software engineering fundamentals, including data structures, design patterns, version control (Git), CI/CD, testing, and monitoring. Exceptional problem-solving skills, with a proven ability to navigate ambiguity More ❯