NeuroJob AI: LLM-Driven Candidate-Job Fit & Automated ATS Keyword Optimization Architecture

`# NeuroJob AI: LLM-Driven Candidate-Job Fit & Automated ATS Keyword Optimization Architecture

Author: Lakshya Kurup Published: January 2026 | Repository: GitHub

Job applicants frequently face high rejection rates due to non-standardized resume formats and sub-optimal Applicant Tracking System (ATS) keyword alignment. Traditional keyword matchers rely on static exact-string matching, missing semantic equivalents, context-aware experience mapping, and tailored interview preparation.

NeuroJob AI addresses these challenges by implementing an end-to-end recruitment assistant powered by the Gemini API, Next.js, TypeScript, and Python. The platform provides automated resume-to-job description fit scoring, tailored cover letter synthesis, ATS keyword gap analysis, and predictive interview question generation within a single, highly responsive interface.

The system is built on a modular client-server architecture designed for low-latency prompt execution and structured schema outputs:

The engine evaluates candidate profiles against targeted job descriptions across 4 core phases:

Published by Lakshya Kurup — Open Source Technical Documentation. `