Deep learning (DL) methods have shown great promise for accurately predicting hydrologic processes but have not yet reached the complexity of traditional process-based hydrologic models (PBHM) in terms of representing the entire hydrologic cycle. The ability of PBHMs to simulate the hydrologic cycle makes them useful for a wide range of modeling and simulation tasks, for which DL methods have not yet been adapted. We argue that we can take advantage of each of these approaches to couple DL methods into PBHMs as individual process parameterizations. We demonstrate that this is viable by developing DL process parameterizations for turbulent heat fluxes and couple them into the Structure for Unifying Multiple Modeling Alternatives (SUMMA), a modular PBHM modeling framework. We developed two DL parameterizations and integrated them into SUMMA, resulting in a one way coupled implementation (NN1W) which relies only on model inputs and a two-way coupled implementation (NN2W), which also incorporates SUMMA-derived model states. Our results demonstrate that the DL parameterizations are able outperform calibrated standalone SUMMA benchmark simulations. Further we demonstrate that the two-way coupling can simulate the long-term latent heat flux better than the standalone benchmark. This shows that DL methods can benefit from PBHM information, and the synergy between these modeling approaches is superior to either approach individually.