or multiple systems. You know how to create repeatable and reusable products. Experience with workflow management tools such as Nextflow, WDL/Cromwell, Airflow, Prefect and Dagster Good understanding of cloud environments (ideally Azure), distributed computing and scaling workflows and pipelines Understanding of common data transformation and storage formats, e.g. More ❯
industry. Conducted and analysed large scale A/B experiments Experience mentoring team members Experience with workflow orchestration technologies such as Airflow, Dagster or Prefect Experience with technologies such as: Google Cloud Platform, particularly Vertex AI Docker and Kubernetes Perks of joining us: Company pension contributions at 5% Individualised training More ❯
of common data transformation and storage formats, e.g. Apache Parquet. Good understanding of cloud environments (ideally Azure), and workflow management systems (e.g. Dagster, Airflow, Prefect). Follow best practices like code review, clean code and unit tests. Familiar with version control and Git/GitHub. Understanding of containerisation (e.g. Docker More ❯
control Setting up monitoring and alerting frameworks to track model drift, data quality, and inference health Leveraging orchestration tools such as Dagster, Airflow, or Prefect to manage and scale ML workflows Supporting ongoing infrastructure migration or optimisation initiatives (e.g. improving cost efficiency, latency, or reliability) Partnering with product and engineering … on AWS , using services like ECS, EKS, Fargate, Lambda, S3, and more Familiarity with orchestration and workflow scheduling tools such as Dagster , Airflow , or Prefect Knowledge of CI/CD best practices and tools (e.g. GitHub Actions, Jenkins, CodePipeline) Exposure to monitoring and observability tools for ML systems (e.g. Prometheus More ❯
