1 of 1 Remote MLOps Jobs in Hertfordshire

feature engineering and data pipeline processes, ensuring reproducibility and auditability. Implementing monitoring and observability to detect drift, bias, and performance degradation, and setting up rollback/recovery processes. Using MLOps tools (e.g., Vertex Pipelines, Kubeflow, Weights & Biases) for experiment tracking, model registry, and automated deployment. Leveraging Docker/Kubernetes and workflow orchestration tools (Airflow, Prefect, Dagster). Collaborating with product … and maintaining ML systems in production, ideally in larger, mature organizations or teams operating at significant scale (e.g., web-scale, distributed systems, cloud-native environments). Brings expertise in MLOps: CI/CD pipelines, Docker, Kubernetes, workflow orchestration (Airflow, Prefect), and automation. Has experience across and understands the full ML lifecycle. Can design for long-term scalability, reliability, and resilience. … and motivated to learn emerging ML engineering tools. Has experience working within cross-functional teams and collaborating across teams. Keeps abreast of the latest advancements in machine learning engineering, MLOps, and generative AI. We would love someone to have any of the following Bachelor's, Master's, or PhD in Computer Science, Engineering, Data Science, or a related STEM subject More ❯