A / 02 / AI-powered career toolkit
ResumeIQ
A resume workbench that compares a candidate’s experience with a job description, offers model-assisted feedback, and exports a PDF.
- React
- FastAPI
- FastEmbed
- ReportLab
Problem
A resume may describe relevant work in different terms from a job listing. A useful comparison needs to surface that relationship and make gaps understandable.
Context and constraints
The repository documentation calls the toolkit ResumeMatcher. It accepts PDF or pasted resume text and keeps scoring, optional model feedback, and PDF export as separate steps.
Role and contribution
The repository provides implementation evidence. Sajid’s individual contribution and release responsibilities are pending confirmation.
Solution
The analysis flow reports semantic fit, skill coverage, and experience quality. Separate endpoints provide language-model feedback and render downloadable resume PDFs.
Architecture
A React frontend calls a FastAPI backend. The scorer extracts text, embeds it with FastEmbed and a BGE model, and uses cosine similarity for semantic comparisons. Model adapters include Gemini. ReportLab generates the PDF.
Engineering decisions
The scoring logic distinguishes skills mentioned in work experience from skills listed without supporting context. PDF export can run independently of model feedback.
H / Project exhibit
Visual showcase
Implementation evidence
The repository contains upload and score endpoints, the embedding-based scorer, provider adapters, and the PDF generation endpoint. Scoring accuracy and job-search outcomes are pending confirmation.