AI Resume Parser & Evaluator
Publicada el 2026-07-27
Descripción de la oferta
I’m building a recruitment pipeline that needs to automatically read a resume, pull out every useful data point, and provide an instant snapshot of how strong the candidate really is. The focus is strictly on resumes—no cover letters or profile pages for now. Here’s what the parser must handle: detect and normalise contact information, break down work experience (company, role, dates, achievements), capture education details, and—crucially—mine every skill keyword or phrase that helps judge competency. On top of the raw extraction I also need an AI layer that assigns a clear Skill-Level assessment and an Experience-Relevance score, both expressed on a consistent numeric scale so recruiters can filter candidates at a glance. I’m comfortable if you build this in Python and lean on NLP libraries such as spaCy, transformers, or similar, provided the final solution can be called through a simple REST or local function call. Deliverables • Clean, well-documented code that ingests common resume formats (PDF, DOCX, TXT) and returns a structured JSON with sections: contact_information, work_experience, education_details, extracted_skills, skill_level_score, experience_relevance_score. • A concise README explaining setup, model choice, and how each score is calculated. • Five sample resumes with their generated JSON so I can validate accuracy (≥90 % correct field extraction on the samples will be the acceptance benchmark). If you’ve built similar parsers, feel free to mention your approach—otherwise outline the models or rule sets you’d start with and the expected turnaround time so we can move ahead quickly.
Skills
Fuente original: freelancer