Own and prioritise the ML backlog for vision models: capture field data and feedback, define labelling taxonomy, prepare training datasets, coordinate labelling teams, and translate on-site observations into ML requirements. Interface between customers, deployment, sales and ML engineers to run cadence, review model feedback, lead new-material development, and ensure model improvements meet operational needs.