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BeginnerPhase 3 8 hours(broken down below)

AI Resume Reviewer

Build an AI-powered resume analyzer that scores resumes, suggests improvements, and tailors feedback for specific job descriptions.

PythonOpenAI APIFastAPIReact

Project walkthrough

AI Resume Reviewer

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Project Goal

Score resumes against job descriptions with explainable, bias-aware feedback.

  • →Parse PDF resumes into structured sections
  • →Align skills to JD requirements with rubric scoring
  • →Generate actionable bullet rewrites
  • →Deliver results via a clean React dashboard

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Time breakdown (8h)

Each phase maps to the estimated hours — follow in order for a realistic build schedule.

Parser & upload API

2h
  • •PDF/DOCX text extraction with section detection
  • •FastAPI upload endpoint with file validation
  • •Store parsed sections in session state

JD alignment & scoring

2h
  • •Extract requirements from pasted JD text
  • •Compute skill-match scores across 5 rubric dimensions
  • •Return structured JSON with per-dimension explanations

LLM feedback generation

2h
  • •Prompt for bullet rewrites with quantified impact
  • •Apply bias filter stripping protected-attribute signals
  • •Add audit log for recruiter review

React UI & polish

2h
  • •Heatmap UI for skill match per dimension
  • •Side-by-side original vs suggested bullets
  • •Error handling for malformed PDFs

Architecture

Resumes and job descriptions enter separate parsing pipelines that extract sections, skills, and requirements. An alignment engine compares extracted entities via embedding similarity and keyword rules, then a rubric scorer produces five dimension scores before the LLM generates bullet rewrites filtered for bias.

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Prerequisites

  • Python 3.11+ and basic FastAPI routing
  • OpenAI API key with structured output (JSON mode)
  • Understanding of PDF text extraction (PyMuPDF or pdfplumber)
  • Familiarity with React forms and file upload
  • Basic knowledge of hiring rubrics and ATS concepts

Setup steps

  1. Create a virtualenv and install FastAPI, PyMuPDF, openai, and pydantic
  2. Scaffold FastAPI with /upload-resume and /score endpoints
  3. Add a React frontend with drag-and-drop PDF upload
  4. Configure OpenAI with a JSON schema for rubric scores
  5. Seed 3 sample resumes and 2 job descriptions for local testing
  6. Run uvicorn and verify end-to-end upload → score flow

Features to build

  • PDF resume parsing
  • ATS score calculation
  • Job description matching
  • Improvement suggestions

Expected result

Demo uploading a resume PDF and a job description, then show a scored report with ATS-style heatmap, gap analysis, and 3–5 rewritten bullet suggestions — all with explainable per-dimension scores.

Resume bullet points

  • →Built an AI resume reviewer using GPT-4 with structured output parsing
  • →Implemented PDF extraction pipeline processing 50+ resume formats

Interview questions

How do you mitigate hiring bias in resume AI?
Exclude protected-attribute features from scoring, use transparent rubric dimensions tied to job requirements, audit score distributions on synthetic test sets, and keep humans in the loop for final hiring decisions.
Why use structured JSON output instead of free-text scores?
Structured output enables consistent UI rendering, downstream ATS integration, and automated evaluation against gold rubric labels — free text is harder to parse and compare across sessions.
How would you handle a PDF that fails to parse?
Fall back to OCR for scanned PDFs, return a clear error with supported formats, and allow plain-text paste as an alternative input path.