AI-Powered Resume Screener using GPT-4 + Vector Search

Description

Design an AI solution that automates resume screening by evaluating candidates’ qualifications, skills, and experiences against job descriptions. Using GPT-4 combined with vector search, this tool understands the semantic context of both resumes and job requirements, ranking candidates based on relevance. HR teams can search for candidates using natural language prompts like “Find me resumes with strong Python, ML, and research experience.” It reduces hiring time, improves shortlisting accuracy, and ensures fairer, bias-reduced assessments. Ideal for recruitment agencies, HR tech startups, and internal HR teams of mid to large enterprises looking to streamline their talent acquisition workflow.

Key Features:

• Multi-format Resume Parsing – Extracts structured data from PDFs, DOCX, and LinkedIn exports with named entity recognition.
• Semantic Matching Engine – Compares resumes with job descriptions using embeddings and GPT-4 scoring logic.
• Natural Language Resume Search – Allows recruiters to query resumes conversationally based on skillsets, experience, or location.
• Bias Mitigation & Fair Ranking – Configurable filters to de-emphasize gender, age, or unrelated data during screening.

Ideal Use Cases or Scenarios:

• HR tech startups building resume intelligence platforms
• Enterprises automating internal resume shortlisting
• Recruitment firms managing bulk hiring drives
• Universities helping students match to internships and jobs

Deliverables:

• End-to-end pipeline: Resume parsing, vectorization, and GPT-4 matching
• Admin dashboard for uploading resumes and job descriptions
• Natural language query interface with filter options
• Vector database (FAISS or Chroma) with scalable indexing
• Project documentation with deployment guide, architecture, and prompt templates
• Optional analytics dashboard for recruiter insights