Year: 2025
Type: article
Abstract: Job seekers frequently encounter challenges such as outdated job listings, lack of real-time updates, complex user interfaces, and limited filtering options. Existing job search platforms often fail to provide a personalized and seamless experience, leaving candidates struggling to find relevant opportunities efficiently. In this work, we address these challenges by developing *Skill Hunt*, a modern, user friendly mobile application that streamlines the job search process through the integration of real-time job listings, salary insights, and user-specific features like personalized dashboards and secure authentication. Skill Hunt is built using *Kotlin and Jetpack Compose*, incorporating *Rapid API* for real-time job data and *Firebase* for authentication and secure profile management. The application undergoes meticulous development, including UI/UX optimization, advanced search filtering for salary, location, and job type, and a personalized recommendation system. Machine learning models may be explored in future iterations to further enhance job matching capabilities. Rigorous testing and performance optimization ensure a smooth user experience, while adherence to*Google Play Store guidelines* facilitates seamless deployment. Our methodology is refined through close collaboration with industry experts and job seekers, ensuring practical usability and effectiveness. The findings of this project hold significant potential to enhance job search efficiency, enabling candidates to discover suitable roles more accurately while providing recruiters with better-matched applicants. Furthermore, Skill Hunt’s user-centric approach streamlines the hiring process, improving engagement and reducing job search fatigue. The successful implementation of this application marks a significant step towards modernizing job search platforms, ultimately benefiting both job seekers and employers.
DOI: 10.46632/cset/3/2/17
Language: English
Topic: Mobile and Web Applications
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4410747041