"Sign Language Recognition (SLR) plays a crucial role in enhancing communication accessibility for deaf and hard-of-hearing communities. This paper introduces an energy-efficient, end-to-end SLR pipeline optimized for real-time edge deployment. Our approach centers on a novel hybrid architecture that integrates MaskedConv1D layers with a Bidirectional Long Short-Term Memory (BiLSTM) network, further enhanced by an attention mechanism to effectively extract and leverage spatio-temporal features from sign gesture sequences. The pipeline incorporates a robust preprocessing module utilizing MediaPipe-based landmark extraction and a selective temporal sampling strategy, which together reduce input redundancy while preserving critical gesture dynamics. Additionally, a lightweight, prompt-driven language model is employed for on-the-fly grammatical correction and translation, ensuring high semantic fidelity under computational constraints. Experimental evaluations on the SSL400 dataset demonstrate competitive classification accuracy, low computational overhead, and high inference speed, making the system well-suited for resource-limited edge devices. These contributions provide a scalable foundation for practical SLR applications and highlight future enhancements in multimodal fusion and on-device language processing."