17 of 17 Remote Kubeflow Jobs in the UK

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 ❯

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 ❯

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 ❯

You will have relevant academic (research Masters level) and/or industry experience. Essential Skills Excellent knowledge and experience of managing an on-premise Kubenetes cluster. Excellent knowledge of Kubeflow and similar systems, e.g. MLflow Good programming ability in Python with familiarity with Linux systems including scripting and system configuration. Experience using AWS, e.g, Cognito, S3, EC2, Lamdas, etc. Experience More ❯

What wed like to see from you: Extensive experience designing and deploying ML systems in production Deep technical expertise in Python and modern ML tooling (e.g. MLflow, TFX, Airflow, Kubeflow, SageMaker, Vertex AI) Experience with infrastructure-as-code and CI/CD practices for ML (e.g. Terraform, GitHub Actions, ArgoCD) Proven ability to build reusable tooling, scalable services, and resilient More ❯

What wed like to see from you: Extensive experience designing and deploying ML systems in production Deep technical expertise in Python and modern ML tooling (e.g. MLflow, TFX, Airflow, Kubeflow, SageMaker, Vertex AI) Experience with infrastructure-as-code and CI/CD practices for ML (e.g. Terraform, GitHub Actions, ArgoCD) Proven ability to build reusable tooling, scalable services, and resilient 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 ❯

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 Modelling (MMM). Real-time personalisation and bidding. Audience segmentation and 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 ❯

and new tools Required Skills and Experience Academic background (research Masters level) or industry experience in a relevant field Strong experience managing on premise Kubernetes clusters Deep knowledge of Kubeflow or similar systems such as MLflow Proficient in Python and experienced with Linux systems Familiar with AWS services such as Cognito, S3, EC2 and Lambda Experience working with ML frameworks More ❯

You will have relevant academic (research Masters level) and/or industry experience. Essential Skills Excellent knowledge and experience of managing an on-premise Kubenetes cluster. Excellent knowledge of Kubeflow and similar systems, e.g. MLflow Good programming ability in Python with familiarity with Linux systems including scripting and system configuration. Experience using AWS, e.g, Cognito, S3, EC2, Lamdas, etc. Experience More ❯

and on-prem hardware. They use both on-prem, self-managed systems and also leverage AWS infrastructure. Essential: Experience of managing an on-prem Kubernetes clusters. Working knowledge of Kubeflow and other systems such as MLflow Exposure to cloud based systems e.g. AWS, Azure or GCP (they use AWS) Knowledge of Linux systems including scripting and system configuration. Understanding of More ❯

city. There is no on-call expectation outside of core office hours . Key Responsibilities: Manage and maintain on-premise Kubernetes clusters Implement and maintain ML Ops pipelines using Kubeflow and similar tools (e.g., MLflow) Develop scripts and tooling in Python; manage Linux system configurations Leverage AWS infrastructure (Cognito, S3, EC2, Lambda, etc.) Integrate ML toolkits (e.g., PyTorch, Lightning) into … various technologies Contribute to the technical strategy and suggest improvements to the ML Ops stack Required Skills and Experience: Solid experience managing on-premise Kubernetes clusters Strong knowledge of Kubeflow or similar ML Ops platforms Proficiency in Python programming, Linux systems, and scripting Experience with AWS services (Cognito, S3, EC2, Lambda, etc.) Familiarity with ML frameworks such as PyTorch or More ❯

city. There is no on-call expectation outside of core office hours . Key Responsibilities: Manage and maintain on-premise Kubernetes clusters Implement and maintain ML Ops pipelines using Kubeflow and similar tools (e.g., MLflow) Develop scripts and tooling in Python; manage Linux system configurations Leverage AWS infrastructure (Cognito, S3, EC2, Lambda, etc.) Integrate ML toolkits (e.g., PyTorch, Lightning) into … various technologies Contribute to the technical strategy and suggest improvements to the ML Ops stack Required Skills and Experience: Solid experience managing on-premise Kubernetes clusters Strong knowledge of Kubeflow or similar ML Ops platforms Proficiency in Python programming, Linux systems, and scripting Experience with AWS services (Cognito, S3, EC2, Lambda, etc.) Familiarity with ML frameworks such as PyTorch or More ❯

scalable ML/AI systems in production environments • Build and deploy robust machine learning solutions using Python • Architect and manage Kubernetes-based ML infrastructure • Leverage MLOps frameworks (e.g., KServe, Kubeflow, MLflow) for end-to-end model lifecycle management • Contribute to and lead open source initiatives with visible community impact • Mentor junior engineers and guide technical direction • Implement and maintain CI … years in ML/AI systems in production • Strong Python development skills • Deep knowledge of Kubernetes and ML workload orchestration • Proven use of MLOps tools such as KServe, Kubeflow, or MLflow • Demonstrated leadership in open source communities • Experience with distributed system architecture • Solid foundation in engineering best practices Location Remote role working European hours . Applications are welcome from professionals More ❯