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

Description and use

NeuroJob AI leverages Gemini API and Next.js to assess resume-job fit, identify keyword gaps, generate cover letters, and produce interview questions, cutting processing time to under 3 seconds.

Problem

Job applicants face high rejection rates due to non-standardized resume formats and sub-optimal ATS keyword alignment.

Outcome

The platform integrates multiple recruitment features into a single web application, reducing evaluation time to under 3 seconds and ensuring zero-error JSON schema responses for deterministic UI rendering.

Workflow

• Upload resume and job description
• Gemini API parses and scores semantic fit
• Keyword gap analysis identifies missing terms
• Cover letter is generated based on fit and gaps
• Interview questions are generated for preparation

Evidence

Job applicants frequently face high rejection rates due to non-standardized resume formats and sub-optimal Applicant Tracking System (ATS) keyword alignment.

Source

NeuroJob AI addresses these challenges by implementing an end-to-end recruitment assistant powered by the Gemini API, Next.js, TypeScript, and Python.

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The engine evaluates candidate profiles against targeted job descriptions across 4 core phases: Semantic ATS Fit Scoring, Keyword Gap Analysis, Tailored Cover Letter Generation, Predictive Interview Prep.

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Reduced full resume-to-JD evaluation time to under 3 seconds using optimized Gemini API streaming logic.

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