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@article{200934,
        author = {Shounak Sunil Kale and Prof. Dr. Mrs. Swapna Bhavsar and Harshal Kotkar and Aniket Bankar and Sameer Bagul},
        title = {Jobify: Auto Job Applyer},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {3571-3575},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200934},
        abstract = {Traditional job search platforms often fail to bridge the gap between candidate profiles and industry requirements, leading to manual fatigue and poor alignment. This paper introduces Jobify, an intelligent ecosystem designed to transition the job application process from a manual "search and apply" model to an automated "discovery and submission" framework. Utilizing Natural Language Processing (NLP) and Word2Vec embeddings, Jobify performs semantic matching between resumes and job descriptions to ensure high-fidelity alignment based on competence rather than mere keyword frequency. Data is dynamically aggregated via a robust web-scraping engine powered by BeautifulSoup and Selenium. By integrating AI-driven email automation and real-time career intelligence, the system significantly reduces cognitive load for applicants while maintaining a personalized outreach. Preliminary results suggest that Jobify optimizes the application lifecycle, allowing professionals to focus on interview performance rather than repetitive administrative tasks.},
        keywords = {Artificial Intelligence (AI), Natural Language Processing (NLP), Word2Vec Embeddings, Web Scraping, Semantic Matching, Autonomous Job Application, Information Retrieval},
        month = {May},
        }