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Resume Analyzer
An AI-powered tool that analyzes PDF resumes against job roles and required skills using local LLMs.
Project Overview
Resume Analyzer is a Django-based application that uses local LLM inference via llama-cpp-python to evaluate PDF resumes. Users can define job roles and required skills, then receive structured insights on candidate fit, strengths, and gaps. The focus is privacy-preserving analysis while generating practical, recruiter-friendly feedback.
Engineering Architecture & Notes
Architecture
Django + DRF API with a Postgres-backed analysis store; local LLM inference via llama-cpp-python.
Data Model
ResumeUpload, AnalysisRun (status + timestamps), AnalysisResult (structured scores + extracted fields).
Performance & Reliability
Avoids re-parsing by persisting extracted text; handles malformed PDFs with clear errors and fallback extraction where possible.
Engineering Tradeoff
Stored structured outputs (not raw generations) for repeatable comparisons; used simple polling for status to keep integration minimal.
Key Features
- Upload and analyze PDF resumes
- Role-based evaluation using local LLMs
- Skill matching and gap identification
- Structured, recruiter-friendly insights
- Runs LLM inference locally using llama-cpp-python
- Built with Django for easy deployment and extension