Recruitment-related scams have increasingly posed serious concerns, often resulting in financial harm and emotional distress for job applicants. Conventional fraud detection techniques, which primarily rely on analyzing textual job content, tend to fall short when dealing with subtle language manipulations or adversarial changes. To address these limitations, this study introduces a custom-built Liquid Neural Network (LNN) model tailored for classifying job listings. LNNs are particularly suited for handling complex and variable language patterns due to their dynamic architecture. The detection pipeline includes essential preprocessing steps like tokenization, lemmatization, and the elimination of stopwords. It further explores feature representation using both TF-IDF (Term Frequency Inverse Document Frequency) and label encoding to assess their relative performance. The proposed LNN model achieved a balanced accuracy of 95.64%, outperforming conventional classifiers like Random Forest and AdaBoost. Experimental evaluation indicates that the proposed method reliably distinguishes fraudulent listings, offering an accurate and lightweight solution to mitigate recruitment fraud.