AI-Powered Interview Preparation Platform

ResumeAnalyzer AI Personalized Interview Preparation

Analyze resumes and job descriptions to generate AI-powered interview questions, skill gap analysis, match scores, and personalized preparation plans.

ReactNode.jsExpress.jsMongoDBGemini AIDockerAWS

Project Overview

ResumeAnalyzer AI is a full-stack AI-powered platform that helps candidates prepare for interviews by analyzing resumes and job descriptions. The application generates personalized interview reports, including match scores, technical and behavioral questions, skill gap analysis, and a structured preparation roadmap.

Technical Stack

Frontend

React, Vite, Axios, Sass

Backend

Node.js, Express.js

Database

MongoDB with Mongoose

AI Engine

Gemini AI for report generation

Authentication

JWT and secure session handling

Deployment

Docker, AWS ECS, ECR, ALB and Vercel

Key Features

1. Resume & Job Analysis

  • Upload resumes and provide target job descriptions
  • Extract candidate skills and experience automatically
  • Compare profiles against job requirements

2. AI-Powered Interview Report

  • Generate technical interview questions with answers
  • Create behavioral and situational interview questions
  • Provide interviewer intent and evaluation criteria

3. Skill Gap Analysis & Study Plan

  • Identify missing or weak skills
  • Classify gaps by severity level
  • Generate personalized day-wise preparation plans

Technical Challenges & Solutions

1. AI Prompt Engineering & Structured Output

Challenge:

Generating consistent and structured interview reports from AI responses while handling different resume formats and job descriptions.

Solution:

Designed reusable prompt templates and implemented validation logic to ensure reliable AI-generated responses and report formatting.

2. Scalable Full-Stack Architecture

Challenge:

Managing resume uploads, AI processing, authentication, and report generation within a scalable architecture.

Solution:

Implemented a modular React and Express architecture, secured APIs using JWT authentication, and deployed containerized services using Docker and AWS ECS.

Key Learnings

AI Integration

  • Prompt engineering
  • Structured AI outputs
  • Resume and job description analysis
  • Structured AI response handling

Full-Stack Development

  • Building secure REST APIs
  • JWT authentication
  • MongoDB schema design
  • Scalable application architecture

Cloud & DevOps

  • Docker containerization
  • AWS ECS deployment
  • Amazon ECR integration
  • Production-ready deployments

Impact & Results

  • Generated personalized interview reports using AI
  • Automated resume and job description analysis
  • Built a secure and scalable full-stack architecture
  • Deployed production-ready services using Docker and AWS

Future Improvements

  • Support multiple AI models
  • Real-time interview simulation
  • Resume version comparison
  • Advanced analytics and progress tracking