The rapid shift to online hiring has opened the door wide for employment scammers, who exploit the scale and anonymity of digital job platforms to target unsuspecting applicants. It is hard to tell the difference between fake and real listings just by looking at them, which makes manual review both unreliable and impossible to do on a large scale. This paper presents a comprehensive deep learning system designed explicitly to identify fraudulent job postings prior to their dissemination to job seekers. The Employment Scam Aegean Dataset (EMSCAD) is used to train the system. It has about 18,000 real job postings, but only about 5% of them are fake. A Bidirectional Long Short-Term Memory (Bi-LSTM) neural network is the heart of the detection engine. It reads job descriptions in both forward and backward directions, picking up on language patterns that single-direction models often miss. To fix the imbalance problem, class-weighted training was used during the learning phase. A FastAPI backend and a full-stack JavaScript frontend connect to the trained model. This lets you analyse individual postings in real time and process CSV datasets in batches. The system does very well on the EMSCAD benchmark, which shows that NLP models that are aware of sequences can be useful tools for fighting employment scams.