AI Resume Analyzer

An AI-powered, full-stack web application that analyzes a resume against a job description and returns a match score, missing skills, and actionable improvement suggestions using a Large Language Model (LLM).

This project focuses on practical AI integration and real-world system design, rather than academic machine learning or model training.

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🚀 Live Demo

• Frontend: Coming soon
• Backend API: Coming soon

Live links will be added once deployment is complete.

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🧠 What This Application Does

Given:

• A resume (plain text)
• A job description

The system returns:

• A match score (0–100)
• A list of missing or weak skills
• Actionable improvement suggestions
• A concise final verdict from a recruiter’s perspective

This mirrors how modern AI-powered tools are used in hiring platforms, HR tech, and internal developer tooling.

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🏗️ Architecture Overview

Frontend (Next.js)
        |
        |  HTTP (JSON)
        v
Backend API (FastAPI)
        |
        |  Prompt + Context
        v
Large Language Model (LLM)

• The frontend handles user input and result presentation
• The backend validates requests and orchestrates AI inference
• The AI model performs structured analysis using prompt engineering

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🛠️ Tech Stack

Frontend

• Next.js (App Router)
• TypeScript
• Tailwind CSS
• Fetch API

Backend

• FastAPI
• Python
• Pydantic (request validation)
• Uvicorn (ASGI server)

AI

• Large Language Models via API
• Structured prompt engineering with enforced JSON output

Deployment

• Frontend: Vercel
• Backend: Render

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📂 Project Structure (Monorepo)

ai-resume-analyzer/
├── backend/
│   ├── main.py
│   ├── services/
│   ├── requirements.txt
│   └── .env.example
├── frontend/
│   └── (Next.js application)
├── README.md
└── .gitignore

The frontend and backend are developed in a single repository and deployed independently.

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🔍 Key Engineering Decisions

Why FastAPI?

• Strong request validation with Pydantic
• Automatic interactive API documentation (/docs)
• Excellent fit for AI-driven and data-oriented services
• Minimal boilerplate with production-grade structure

Why Use LLM APIs Instead of Training a Model?

• Reflects how AI is used in real-world products
• Faster iteration and significantly lower operational cost
• Focuses on AI system design, not model research

Why Enforce Structured JSON Output?

• Predictable frontend rendering
• Easier validation and error handling
• Prevents malformed or ambiguous AI responses
• Safer than free-form text generation

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📦 API Example

Endpoint

POST /analyze-resume

Request

{
  "resume": "Experienced backend engineer with Python and APIs...",
  "job_description": "Looking for a Python developer with cloud experience..."
}

Response

{
  "match_score": 78,
  "missing_skills": ["Docker", "AWS"],
  "suggestions": [
    "Add quantified achievements",
    "Highlight cloud-related projects"
  ],
  "verdict": "Strong candidate with minor gaps"
}

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🔐 Environment Variables

The backend requires environment variables for secure configuration.

Example:

OPENAI_API_KEY=your_api_key_here

Environment files are excluded from version control.

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🧪 Running Locally

Backend

cd backend
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
uvicorn main:app --reload

Visit:

• http://127.0.0.1:8000 — API root
• http://127.0.0.1:8000/docs — Interactive API documentation

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📈 Future Enhancements (Non-Breaking)

The current architecture supports future extensions such as:

• PDF resume upload
• Resume history and comparisons
• Retrieval-Augmented Generation (RAG)
• Authentication
• Analytics and feedback tracking

These additions can be introduced without rewriting the core system.

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📜 License

MIT