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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 Abstract 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. 1. System Architecture & Tech Stack The system is built on a modular client-server architecture designed for low-latency prompt execution and structured schema outputs: Component Technology Role Frontend Next.js (App Router), React, TypeScript, Tailwind CSS Dynamic user dashboard, ATS scoring meters, and live breakdown rendering AI Integration Gemini API (Google AI Studio) Structured output parsing, semantic evaluation, and natural language synthesis Processing Engine Python, Node.js Document text parsing, sanitization, and structured payload preparation Version Control Git / GitHub Code maintenance and CI/CD deployment 2. Core Feature Pipeline The engine evaluates candidate profiles against targeted job descriptions across 4 core phases: Semantic ATS Fit Scoring: Evaluates candidate resume text against job descriptions using structured LLM parsers to compute a normalized match percentage (0–100%). Keyword Gap Analysis: Highlights missing industry keywords, required technical skills, and domain-specific acronyms missing from the candidate profile. Tailored Cover Letter Generation: Synthesizes custom, context-aware cover letters aligning candidate achievements with the job requirements. Predictive Interview Prep: Generates role-specific behavioral and technical interview questions tailored to identified resume gaps and required skills. 3. Key Results & Efficiency Metrics Multi-Feature Integration: Combined fit scoring, cover letter writing, keyword gap detection, and interview prediction into a single web application. Low Latency Workflows: Reduced full resume-to-JD evaluation time to under 3 seconds using optimized Gemini API streaming logic. Structured Payload Reliability: Enforced zero-error JSON schema responses for deterministic UI component rendering. Published by Lakshya Kurup — Open Source Technical Documentation.`

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