Einstein International Journal Organization(EIJO)

The proliferation of digital recruitment platforms has fundamentally transformed job-seeking, compelling candidates to manage numerous concurrent applications across disparate portals and timelines. Existing approaches—including spreadsheets and generic project-management tools—offer no automation, no intelligent communication assistance, and no analytical feedback. This paper presents Job Pilot AI, a cloud-deployed full-stack web application that consolidates job application management into a single intelligent platform. The system integrates a Kanban-style drag-and-drop workflow engine, an SMTP-based automated reminder subsystem, an AI-powered follow-up message generator driven by a large language model (LLM) API, and a real-time analytics dashboard. The frontend employs Next.js 14 and React.js, deployed on Vercel's global CDN. The backend exposes a versioned RESTful API in Node.js and Express.js, with MongoDB Atlas as the data layer. Security is enforced through JWT-based HTTP-only cookie authentication and Google OAuth 2.0. Production benchmarks yielded a Lighthouse performance score of 87, accessibility score of 93, average API latency of 245 ms, and email delivery within 12 seconds. All 38 functional test cases passed, and user acceptance testing returned an overall satisfaction score of 4.5/5.0.

Keywords: Job Application Tracker, Kanban Workflow, Artificial Intelligence, Large Language Model, RESTful API, JWT Authentication, MongoDB Atlas, Next.js, Automated Reminder System, Analytics Dashboard, Full-Stack Web Application

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