LLD

AI-Powered Recruitment Automation System

Low-Level Design Document (LLD)

📘 Table of Contents

1. System Overview
2. Module Breakdown
3. API Design
4. Database Design
5. Business Workflow
6. AI Integration Points
7. System Architecture

1. System Overview

1.1 🎯 Purpose

Automate the recruitment pipeline from resume ingestion to candidate ranking using NLP-based parsing, vector embeddings, and AI-powered re-ranking.

1.2 ⚙️ Key Capabilities

• Incremental Resume Ingestion: Fetch new resumes from Gmail on schedule
• Automated Resume Parsing: Extract structured data (skills, experience, education, contact)
• Semantic Matching: Use embeddings to find similar candidates to job descriptions
• AI Re-Ranking: Re-rank top candidates using GPT-4o-mini for better precision
• Dashboard: HR views ranked candidates with explainability

1.3 🧰 Technology Stack

2. Module Breakdown

Module 1: Gmail Module

Responsibility: Email integration and resume fetching

Functions:

• Authenticate with Gmail API using OAuth 2.0
• Fetch emails with attachments based on filters (date, keywords, labels)
• Download resume attachments (PDF, DOCX)
• Track last fetch timestamp for incremental processing

Key Operations:

• fetchNewEmails(afterDate, filters) → Returns array of email objects with attachments
• downloadAttachment(messageId, attachmentId) → Returns file buffer
• getLastFetchTime(jobId) → Returns timestamp of last successful fetch

External Dependencies: Gmail API, Google OAuth 2.0

Module 2: Resume Parser Module

Responsibility: Extract structured data from resume text

Parsing Strategy: Rule-based NLP with regex patterns

Name Extraction: First line heuristics, capitalization patterns
Email Extraction: Regex pattern [a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}
Phone Extraction: Regex for various formats (+1-XXX-XXX-XXXX, (XXX) XXX-XXXX)
Skills Extraction: Keyword matching against predefined skill database
Experience Parsing: Section detection (keywords: "experience", "work history") + date parsing
Education Parsing: Section detection + degree/institution extraction

Functions:

• extractText(fileBuffer, format) → Returns plain text from PDF/DOCX
• parseResume(resumeText) → Returns structured JSON object
• validateParsedData(data) → Returns boolean + error messages
• normalizeSkills(skillArray) → Returns deduplicated, lowercase skills

Output Format (JSON):

{
  "name": "John Doe",
  "email": "john@example.com",
  "phone": "+1-555-0123",
  "skills": ["javascript", "python", "sql"],
  "experience": [
    {
      "title": "Software Engineer",
      "company": "Tech Corp",
      "duration": "2020-2023",
      "description": "Built REST APIs..."
    }
  ],
  "education": [
    {
      "degree": "B.S. Computer Science",
      "institution": "MIT",
      "year": 2018
    }
  ],
  "total_experience_years": 5
}

Alternative AI Approach (Optional Enhancement)

Instead of rule-based parsing, use GPT-4o-mini with structured JSON output. The flow would be: extract plain text -> send it to GPT-4o-mini with a schema-enforced prompt -> receive structured JSON (name, email, phone, skills, experience, education, total years).

Pros:

• Much higher accuracy across different resume formats and layouts
• Handles inconsistent structures, missing sections, and varied wording
• Reduces need for complex regex/heuristics

Cons:

• Incur API usage costs
• Slightly higher latency (~2–5 seconds per resume, depending on length)
• Requires API reliability and internet access