versioning, reproducibility, experimentation, feature management and release management Own and improve the production environment for machine learning systems, ensuring strong standards for availability, performance, observability and resilience Define and implement monitoring across model and platform layers, including system health, data quality, drift, latency, throughput and cost efficiency Build or optimise … pipelines, infrastructure-as-code and workflow orchestration Experience with tools such as Airflow or similar platform and orchestration technologies Good understanding of model observability, data quality, feature pipelines, lineage and reproducibility Experience designing scalable infrastructure for ML workloads, including training, batch inference and real-time serving Strong appreciation of reliability ...