Radar wind profilers (RWPs) traditionally rely on heuristic statistical procedures for signal processing and quality control, often suffering from inherent trade-offs between data availability and retrieval reliability. To address these limitations, this study presents an end-to-end deep learning framework that retrieves wind vectors directly from raw Doppler spectra. Validated against a three-year (2022–2024) dataset of raw spectra from 437.5-MHz and 1290-MHz profiler systems and collocated radiosonde observations, the proposed framework demonstrates robust performance, particularly when trained on the full spectral continuum without filtering ambiguous samples. Notably, we found that hybridizing deep learning with conventional post-processing is highly contingent upon hardware-specific error characteristics; it effectively mitigated transient outliers in the 1290-MHz systems but provided negligible benefits for the range-smeared, vertically coherent artifacts dominating the 437.5-MHz systems. Furthermore, the framework reasonably reproduced seasonal, diurnal, and vertical atmospheric structures without the excessive smoothing typical of conventional statistical approaches. Although constrained by a slight underestimation of extreme wind speeds due to the regression-to-the-mean effect inherent in mean-squared-error optimization, the model notably enhanced operational data availability and integrity, demonstrating the feasibility of hardware-aware deep learning as a viable alternative or complement to conventional RWP processing pipelines.