Hybrid deep learning models aid online employment scam detection. Because they use recurrent and convolutional neural networks, these systems can detect fraud. We can see patterns in user interactions and job ads in place and time. Advanced embedding and preprocessing can handle uneven and noisy datasets. This finding comes from combining company profiles and posting activities with job listing, email, and chat text. Due to the combination, it is now easier to discern ethical from immoral employment practices. Accuracy, recall, F1-score, and ROC-AUC are performance measurements. Experiments show these classifiers outperform machine learning leading models. Explainability tactics help people understand forecasts to build confidence. The system can automatically detect and inform users of scams on online job boards to boost trust.