Reduced turn-around-time by 90% for Mopid’s clientele by developing an advanced resume data extraction method and re-ranking algorithm.

Project Details:

The How:

Instead of the usual keyword match approach from job description to resume for filtering out candidates, I created a method to extract more insights from a resume and an algorithm that made the process of shortlisting the right candidate for a job faster without the need for human intervention.

My Contribution:

Discovery

Identified bottleneck in the candidate screening flow by observing the process and speaking with the operations team.

My research work for optimising the Candidate Screening Flow

My research work for optimising the Candidate Screening Flow

Development

  1. Developed a JSON schema capable of storing categorized resume information.

  2. Created a prompt for analyzing and extracting categorized resume data.

Demo of Resume Analysis for Enrichment

Demo of Resume Analysis for Enrichment

  1. Coded a Python application to generate and store resume data embeddings in a vector database, enabling a semantic search index using Google’s Vertex AI platform.

Python Application for Semantic Search

Python Application for Semantic Search