NORi combines a physics-based ocean boundary-layer mixing scheme with neural networks that learn the entrainment missing from a local diffusive closure. It trains against the evolution of temperature and salinity in large-eddy simulations while conserving tracers by construction. Tests examine unfamiliar forcing conditions and seasonal mixing at Ocean Weather Station Papa, where performance is comparable to established closures. An idealized double-gyre experiment remains numerically stable for 100 years despite training on two-day trajectories, although this does not establish accuracy in a realistic global ocean. Readers get a detailed example of designing, training, and evaluating a hybrid physics-and-ML parameterization that targets missing processes while retaining a simple physical foundation.