ResumeAI Development: AI-Powered Resume Matcher

Try it now – drag-and-drop a PDF/DOCX résumé + job spec, then hit Analyze Match to see real-time scoring and keyword gaps.

Full-stack Web App – Next.js 14 front-end + FastAPI micro-services with interactive Swagger docs.

NLP Skill-Match Engine – Fine-tuned Hugging Face BERT models for contextual keyword matching; cuts screening time ~75 %.

ATS-Style Scoring Dashboard – Shows match %, missing keywords, and semantic relevance just like top ATS scanners.

Dockerized CI/CD – One-command spin-up; auto-deploys to Vercel/Railway for zero-downtime releases.

Enterprise-Grade UX – Responsive React + TypeScript UI, Tailwind CSS, dark-mode ready and WCAG-AA compliant.

ResumeAI gives recruiters the precision of an ATS and candidates the clarity of a résumé coach. Powered by FastAPI-served NLP and a slick Next.js interface, it surfaces skill gaps, boosts match scores, and lets hiring teams shortlist talent in seconds.

Discovery – Benchmarked how leading résumé scanners grade documents.

Model Selection – Evaluated and fine-tuned pre-trained BERT models for skill extraction.

API Layer – Built REST + WebSocket endpoints in FastAPI (< 50 ms inference) with auto-generated OpenAPI docs.

Front-End & UX – Implemented drag-and-drop uploads, progress toasts, and real-time match scores in React/Next.js.

DevOps – Wrote Dockerfiles, GitHub Actions, and Vercel configs for continuous deployment.

Validation – Tested with 30+ résumés across tech, finance, and healthcare to ensure scoring accuracy.

75 % faster résumé screening in pilot tests.

Candidates averaged 18 % higher match scores after iterating with ResumeAI’s suggestions.

Clean single-page UX keeps bounce rates low compared with multi-step competitors.

How is this different from generic résumé scanners? ResumeAI uses contextual embeddings, so synonyms (“lead dev” vs. “senior engineer”) still score.

Can hiring teams integrate it? Yes—FastAPI endpoints are fully documented; drop in an API key and plug it into any HRIS.

Is my data secure? Files are processed in-memory and purged after 30 minutes—nothing is stored on disk.

What’s next on the roadmap? Bulk résumé uploads, multilingual JD parsing, and GPT-generated cover-letter drafts.

Front-End: Next.js 14, React 18, TypeScript, Tailwind CSS

Back-End: FastAPI, Pydantic, Uvicorn

ML/NLP: Hugging Face Transformers, spaCy, scikit-learn

Infrastructure: Vercel edge runtime, Docker, GitHub Actions

Testing: Pytest, React Testing Library, Playwright

With 98 % of Fortune 500 companies relying on ATS filters, ResumeAI bridges the gap between recruiter expectations and candidate reality—delivering data-driven matches that survive real hiring pipelines.