AI Campus • Practical resources
AI Resume & Job Matcher with ATS Scoring
Build an explainable resume-to-job comparison, starting with local parsing and a transparent keyword baseline.
Build guide and prerequisites
Follow this roadmap to implement and validate your own application, then record your observed results.
- Python lists, dictionaries and file handling.
- Your own redacted resume and two job descriptions you can use for practice.
Required tools and free options
- Python
- pdfplumber
- JSON or CSV
- Optional FastAPI and React
Cost: Local text parsing and keyword comparison require no paid API. Optional local semantic models need a download and hardware; hosted model inference is a separate paid option, not required here.
Build it step by step
1. Prepare representative inputs
Create examples covering a relevant role, an unrelated role and a scanned-image resume. Remove phone numbers and addresses from the working fixtures. Save the job descriptions as plain UTF-8 text.
2. Extract and inspect text
Use pdfplumber to extract each page into text while retaining page numbers. Show the extracted text before scoring. If pages have no text, mark the file as requiring OCR instead of scoring it as an unqualified candidate.
3. Create a job requirements list
Manually label required and preferred skills for the first job description in a JSON file. Keep aliases such as JS/JavaScript explicit. Separate years of experience from skills so a single number does not become an invented requirement.
4. Implement an explainable baseline
Normalize case and punctuation, then compare each requirement against the extracted resume. Compute matched-required divided by total-required, guarding against an empty denominator. Display matched and missing evidence alongside any percentage; label it a custom overlap score, not an actual employer ATS score.
5. Add an optional semantic comparison
Only after the baseline works, compare sentence embeddings from a local model. Keep those results separate and inspect false matches manually. Do not infer protected characteristics, suitability or hiring outcomes from a similarity number.
6. Build the result and verify failure cases
Add file-size/type validation and a result page showing parsing status, matched snippets and limitations. Test malformed PDFs, empty descriptions, a synonym case and an irrelevant resume. Export the transparent report without storing uploaded resumes by default.
Expected output and validation
A demonstrable resume comparison report with traceable evidence and a clearly defined custom score. No commercial ATS integration or employment prediction is supplied.
- Every matched requirement links to actual extracted wording.
- Scanned or unreadable PDFs show a parsing warning.
- Changing the job description changes the evidence and score deterministically.
Official references
Free to read. Software setup, model access and hosted services have their own requirements. References checked 3 November 2026.