Career Hunt: Full-stack job board and application management platform

Overview

Career Hunt is a dual-sided platform simulating a real-world startup product. It streamlines the hiring process by providing recruiters with structured pipelines and job seekers with AI-enhanced application tools. The architecture emphasizes scalability and performance, utilizing Docker for environment parity and Redis to handle high-traffic read operations.

The Challenge

Managing job applications via spreadsheets is inefficient for seekers, while recruiters often face friction with overly complex, expensive Applicant Tracking Systems (ATS). There was a need for a lightweight, centralized solution that democratizes access to ATS-like features and leverages AI to reduce the repetitive burden of application tailoring.

Project Goals

• Demonstrate a clean, scalable full-stack architecture using the MERN stack
• Implement secure, multi-provider authentication (Google, GitHub, LinkedIn) via OAuth
• Integrate OpenAI to provide practical utility through resume improvements and cover letter generation
• Establish an end-to-end DevOps workflow using Docker and CI/CD pipelines
• Optimize database load for read-heavy operations using Redis caching

Technical Stack

Frontend

• React (Vite): Fast, modern client-side framework.
• Tailwind CSS: Utility-first styling for responsive design.
• React Router: Client-side routing for seamless navigation.

Backend

• Node.js & Express: Scalable server-side logic and API routes.
• Redis: In-memory data structure store used for caching high-traffic endpoints.

Database

• MongoDB (Mongoose): Flexible NoSQL database for handling variable job schemas.

AI & Auth

• OpenAI API: Powers the generative AI features for resumes and cover letters.
• OAuth: Secure authentication via Google, GitHub, and LinkedIn.

DevOps

• Docker: Containerization for consistent development and production environments.
• GitHub Actions: CI/CD pipelines for automated testing and deployment.
• Vercel & Railway: Hosting solutions for frontend and backend respectively.

Key Features

AI Content Generation

Leverages GPT models to analyze resumes for tone improvements and auto-generate context-aware cover letters.

Recruiter Management Suite

Allows recruiters to post jobs with Markdown support, manage company profiles, and track candidate pipelines.

Performance Caching

Implements Redis caching for job listings, reducing database load by approximately 40% during read-heavy traffic.

Secure Authentication

Scalable OAuth implementation supporting multiple providers (Google, GitHub, LinkedIn) for seamless access.

Challenges & Solutions

Handling variable job description structures

Solution: Adopted a NoSQL approach (MongoDB) to accommodate flexible schemas for job posts (e.g., varying compensation models) without complex migrations.

Optimizing read-heavy endpoints

Solution: Integrated Redis to cache the 'Browse Jobs' data for one hour, significantly reducing latency and server load.

Ensuring cross-environment consistency

Solution: Utilized Docker to containerize the application, ensuring identical runtime behavior across local development (macOS/Windows) and production Linux servers.

Improving AI relevance

Solution: Engineered specific prompts to combine candidate skills with job descriptions, preventing generic AI output and ensuring tailored cover letters.

Roadmap

• Browser extension for one-click job saving
• Automated job import from LinkedIn/Indeed
• Collaborative recruiter dashboards for team-based hiring
• AI-driven mock interview assistant
• Advanced analytics for application conversion rates

Outcome

A robust, Dockerized full-stack application that successfully bridges the gap between simple trackers and complex ATS platforms. It demonstrates proficiency in cloud-native deployment, database optimization, and third-party API integration.