Device - Macbook Air

Challenge / Problem Statement

The recruitment industry faced multiple challenges:

  • Manual resume screening was time-consuming and error-prone.
  • Resumes came in varied formats, complicating parsing and data extraction.
  • Keyword-only searches often missed highly relevant candidates.
  • Lack of intelligent ranking reduced recruiter efficiency.
  • Hiring pipelines struggled with scalability and personalization.

Objectives

  • Automate real-time resume parsing and job description (JD) matching.
  • Ensure accurate and structured candidate data extraction.
  • Rank candidates by fitment score for faster hiring decisions.
  • Improve recruiter efficiency by reducing manual screening workload.
  • Build scalable APIs for seamless integration with existing HR platforms.

Process & Implementation

  • Preprocessed resumes (PDFs) using OpenCV & PyMuPDF.
  • Cleaned and structured data with Pandas & NumPy.
  • Integrated NLP & LLMs for semantic JD–resume matching.
  • Applied cosine similarity and ranking algorithms for candidate scoring.
  • Developed FastAPI-based REST APIs for HR dashboard integration.
  • Generated structured outputs: match scores, missing skills, and summaries.

Our Solution

We engineered an AI-powered recruitment pipeline that automated the resume-to-JD matching workflow:

  • Extracted candidate details from PDF resumes.
  • Normalized and structured candidate profiles.
  • Used ChatGPT-4 & Mini LLMs for semantic similarity with job descriptions.
  • Ranked candidates with fitment scores for recruiter review.
  • Designed APIs for scalable, real-time ATS integration.

Tools & Tech Used

  • Backend: Python, FastAPI
  • AI/LLMs: ChatGPT-4, Mini LLMs
  • Data Processing: Pandas, NumPy
  • Parsing: OpenCV, PyMuPDF/PDFPlumber
  • APIs & Integration: REST, JSON
  • Automation: WebSockets, Celery

Results & Impact

  • Automated 80% of manual resume screening.
  • Delivered structured, AI-ranked candidate lists.
  • Improved accuracy of JD–resume matches.
  • Significantly reduced time-to-hire.
  • Boosted recruiter productivity and hiring outcomes.

Key Takeaways

  • AI + LLMs revolutionize talent acquisition with accuracy and speed.
  • Resume parsing + semantic JD matching ensures better candidate-job alignment.
  • Scalable ATS integration supports multiple HR platforms.
  • Automation reduces costs and accelerates hiring cycles.

Project Duration & Team

  • Duration: 4-5 weeks
  • Team:1 Full-Stack Python Developer, 1 NLP/AI Engineer, 1 QA Analyst
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