Fix Résumé Import & ATS Tailoring

I need an experienced AI-savvy full-stack engineer to tighten up the résumé import and ATS tailoring flow inside OperationMOS. Right now the weakest link is document parsing accuracy: key fields are being extracted wrongly, which in turn throws off every downstream step. I consistently see incorrect extraction of personal information, work-experience lines, and even education details, so fixes must start there.

You’ll dive into our Python/FastAPI backend, React/TypeScript front end, and the LLM-based analysis layer. Your mandate is to make sure every résumé fact survives the entire journey—import, analysis, tailoring, save, and export—without distortion. When you push code, meaningful regression tests and an end-to-end validation path should prove it works.

Before you propose changes, create a free OperationMOS account at staging.operationMOS.com, run the Résumé Import and ATS Tailoring tools with a fictional or redacted résumé, then share up to three evidence-backed findings: clear reproduction steps, expected vs. actual behavior, and your priority call. That submission will show me you’ve already put the system under a microscope.

Deliverables:
• Refactored parsing logic that correctly captures personal information, work history, and education
• Adjustments to the matching and tailoring algorithms so they honor the newly accurate data
• Automated regression test suite and end-to-end validation script
• Concise technical note summarizing the fixes and how to extend them

This module must be completed within one week. Budget is flexible for top-tier results. If you’re comfortable moving between backend, frontend, and LLM integrations—and you relish turning fragile parsers into bulletproof pipelines—this should be a satisfying engagement.