City of London, London, United Kingdom Hybrid / WFH Options
Burns Sheehan
hiring. 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 the Data Scientists Develop and maintain pricing analytics tools that enable faster impact assessments, reducing manual work Collaborate with the technical pricing, street pricing and product teams to More ❯
hiring. 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 the Data Scientists Develop and maintain pricing analytics tools that enable faster impact assessments, reducing manual work Collaborate with the technical pricing, street pricing and product teams to More ❯
hiring. 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 ❯
we’d 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 ❯
we’d 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 ❯
Plumstead, Greater London, UK Hybrid / WFH Options
Canonical
home-based role, we are hiring worldwide. What your day will look like Understand Ubuntu, Linux, networking and services in real-world environments Architect cloud infrastructure solutions like Kubernetes, Kubeflow, OpenStack, Ceph, and Spark either On-Premises or in Public Cloud (AWS, Azure, Google Cloud) Architect and integrate popular open source software such as PostgreSQL, MongoDB, Kafka, Cassandra and NGINX More ❯
convert customer requirements or business challenges into well-defined machine learning solutions We are using many technologies day to day such as various AWS services, GCP, Kubernetes, Ray Serve, Kubeflow, and ReTool. Any experience in these areas would be a bonus Sprout.ai Values Hungry for Growth - Unleash your inner Sprout: Sprouts embrace growth, forget comfort zones, and help Sprout.ai thrive. 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 ❯
Cardiff, South Glamorgan, United Kingdom Hybrid / WFH Options
Starling Bank Limited
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 ❯
Manchester, Lancashire, United Kingdom Hybrid / WFH Options
Starling Bank Limited
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 ❯
pain points and practical solutions Experience building systems for scaling training, versioning, and deployment Bonus points for experience with distributed compute, data engineering, and orchestration frameworks (e.g. Airflow, Ray, KubeFlow). Why join? Top-tier quant finance firm with huge tech investment Competitive base salary + 50–100%+ annual bonus 25 days holiday, monthly WFH allowance, and £20/ More ❯
Opportunity to work for a UK Based Edtech company Salary up to £50k depending on experience Fully remote role Key experience - ML/AI, NLP, LLMs, MongoDB, Agent Architecture, Kubeflow If you wish to keep your CV/data private feel free to WhatsApp your details/CV to me, Dan - WHO WE ARE: We are a start-up Edtech … for us to process (subject to required skills) your application to our client in conjunction with this vacancy only. KEY SKILLS: ML/AI, NLP, LLMs, MongoDB, Agent Architecture, KubeflowMore ❯
