Hiring the right candidate is a tough and time consuming task, especially when HR departments receive hundreds of resumes for a single job post. Going through them manually takes a lot of time and can lead to human bias or errors. To solve this practical problem, we developed Career Navigator, an automated resume screening system. Unlike older Applicant Tracking Systems ATS that only look for exact keywords, our project tries to understand the actual meaning of the text using Natural Language Processing NLP . We used TF IDF to extract important features from the resumes and built a Recurrent Neural Network RNN to classify them into specific job domains. Additionally, we added a recommendation engine using Cosine Similarity to suggest the best fitting jobs and show candidates what skills they are missing. Our tests showed promising results, achieving a classification accuracy of 91.4 while heavily reducing the time needed to screen a profile. Resume Screening, NLP, Deep Learning, RNN, Recommender System, TF IDF. 55-62 Smart Innovations in Computer Science and Applications Special Issue Vandana Dewangan