Projects
Statusneo

AI-Powered HR System with Talent Management

Designed internal HR system with NLP-based skill extraction & JD-CV matching, streamlining recruitment workflows and enhancing hiring decisions through intelligent automation.

2 min read

Impact

Improved talent acquisition efficiency, automated candidate matching

NLP
HR Tech
Resume Parsing
Talent Matching

Overview

Designed an internal HR system leveraging NLP for automated skill extraction and intelligent JD-CV (Job Description-CV) matching. The system enables talent database management, automated candidate-job matching, and recruitment optimization through AI-driven analytics.

Technical Architecture

NLP-Based Resume Parsing

  • Automated resume parsing and information extraction
  • Skill extraction using NER and custom pattern matching
  • Experience and education parsing
  • Standardized candidate profile generation

JD-CV Matching Engine

  • Semantic similarity between job descriptions and resumes
  • Skills-based matching algorithm
  • Experience level matching
  • Cultural fit indicators

Talent Database

  • Centralized candidate database with rich profiles
  • Skill-based search and filtering
  • Talent pool segmentation
  • Historical hiring pattern analysis

Key Features

  • Automated Resume Parsing: Extract structured information from diverse resume formats
  • Intelligent Matching: AI-driven candidate-job matching
  • Skill-Gap Analysis: Identify skill gaps for workforce planning
  • Recruitment Analytics: Data-driven insights for hiring decisions
  • Workflow Automation: Streamlined recruitment processes

Technical Challenges & Solutions

Challenge: Diverse Resume Formats

Resumes came in various formats, structures, and languages.

Solution: Built robust parsing system handling PDFs, Word documents, and images. Implemented OCR for scanned resumes, created format-agnostic information extraction, and used NER with custom rules for structured data extraction.

Challenge: Semantic Job-Candidate Matching

Simple keyword matching missed semantic relationships between skills and requirements.

Solution: Implemented semantic embedding-based matching using sentence transformers. Created skill taxonomy with synonym and related-skill mappings. Designed scoring algorithm considering both required and preferred qualifications.

Challenge: Skill Taxonomy Management

Needed standardized skill names despite varied terminology in resumes.

Solution: Built comprehensive skill taxonomy with synonym mappings, implemented fuzzy matching for skill normalization, and created skills clustering for related competencies.

Impact

  • Improved Talent Acquisition Efficiency: Automated candidate screening reduced time-to-hire
  • Better Hiring Decisions: AI-driven analytics and matching improved hire quality
  • Streamlined Workflows: Automation reduced manual effort in recruitment
  • Skill-Gap Identification: Analytics enabled proactive workforce planning
  • Scalable Talent Management: System grew with organizational hiring needs

Technologies Used

  • NLP: spaCy, Named Entity Recognition, skill extraction
  • ML: Sentence transformers, semantic similarity
  • Resume Parsing: PDFMiner, python-docx, OCR (Tesseract)
  • Database: PostgreSQL for talent database
  • Backend: Python, FastAPI

Innovation

  • End-to-End HR Automation: Complete pipeline from resume intake to candidate matching
  • Semantic Understanding: Beyond keyword matching to skill relationship understanding
  • Analytics Integration: Data-driven insights for strategic workforce planning