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

Lakshya Kurup — Wed, 14 Oct 2026 10:54:02 +0000

`# 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.`

ScamShield: Explainable Real-Time Threat Analysis & Deterministic Risk Scoring Architecture

Lakshya Kurup — Wed, 14 Oct 2026 10:49:00 +0000

`# ScamShield: Explainable Real-Time Threat Analysis & Deterministic Risk Scoring Architecture

Authors: Lakshya Kurup, Rajdeep Sharma

Published: August 2026 | Repository: GitHub

Abstract

Modern social engineering and phishing attacks targeting digital consumers rely heavily on cognitive pressure, brand spoofing, and obscure link shorteners. Traditional detection platforms frequently rely on black-box LLM scoring or delayed blocklists, leading to non-deterministic outputs, API latency, and a lack of actionable transparency for non-technical end-users.

ScamShield (GuardScam AI) addresses these limitations by introducing a hybrid, rule-driven, and explainable threat analysis engine. Built using Next.js 16, TypeScript, FastAPI, Pydantic, SQLite, and Docker, ScamShield provides real-time risk scoring, granular evidence breakdowns, and AI-assisted plain-language summaries across suspicious URLs, emails, SMS, and WhatsApp messages.

1. Introduction & Problem Statement

Phishing vectors are evolving rapidly across non-email channels like SMS, WhatsApp, and messaging apps. Common failure modes in consumer-facing threat protection include:

Black-box decisions: Users receive binary flags (Safe/Unsafe) without granular context on why a message is malicious.
High API overhead & Latency: Directing every inbound string directly to an LLM introduces significant latency and cloud API costs ($0.02–$0.10/request).
Over-reliance on Static Blocklists: Newly registered domain names (Zero-Day phishing sites) bypass domain reputation databases.

ScamShield decouples deterministic heuristic verification from generative summary modules, maintaining execution speeds below 150ms while delivering clear reasoning.

2. System Architecture & Tech Stack

The platform uses a decoupled client-server model optimized for high throughput, low latency, and deterministic analysis.

Component	Technology	Primary Role
Frontend	Next.js 16 (App Router), React, TypeScript, Tailwind CSS	Client Dashboard, Evidence Cards & Plain-Language Breakdown
Backend Framework	Python (FastAPI), Pydantic	Data validation, schema enforcement, heuristic evaluation
Database & Persistence	SQLite, REST APIs	Rule pattern persistence and local threat telemetry storage
Infrastructure	Docker, CORS Middleware, Rate-limiting layers	Containerized deployment, API protection, and scalability

Data Flow Pipeline

Payload Ingestion: Incoming Payload (URL, Email, SMS/WhatsApp) is captured by the endpoint.
Analysis Engine: FastAPI Backend / Pydantic Engine executes:
- Heuristic Rule Evaluation (Urgency, Deceptive Links, Payment Triggers)
- Deterministic Risk Scoring (0–100 Scale)
- Structured Output Generation
Client Presentation: Next.js 16 Client Dashboard renders Evidence Cards & Plain-Language Summaries.

3. Threat Analysis Engine & Heuristics

The engine evaluates incoming payloads across multiple deterministic feature matrices:

Urgency & Pressure Triggers: Regex pattern matching for psychological coercion ("account suspended within 2 hours", "immediate legal action", "OTP required now").
Deceptive URL & Structure Analysis: Detection of typosquatting, sub-domain stacking, IP-based hostnames, and suspicious URL shorteners.
Financial & Credential Harvesting Indicators: Automated identification of unverified banking requests, gift card incentives, and fraudulent payment gateway links.
Deterministic Risk Scoring: Assigns normalized threat scores (0–100) with strict categorization (Safe, Caution, High Risk, Malicious).

4. Key Performance Metrics & Results

Deterministic Execution Speed: Sub-150ms throughput for rule-based heuristics engine.
Cost Efficiency: Decreases generative API overhead by routing structured payloads for AI summarization only when necessary.
Zero Silent Failures: Comprehensive input parsing via Pydantic prevents silent script failures across malformed or obfuscated input payloads.

5. Future Roadmap

Modular integration of local lightweight NLP models for off-grid threat classification.
Browser extension rollout for automated background DOM inspection.
Real-time community reporting loop backed by crowdsourced verification signals.

Published by Lakshya Kurup & Rajdeep Sharma — Open Source Technical Documentation.`

DEV Community: Lakshya Kurup

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

Abstract

1. System Architecture & Tech Stack

2. Core Feature Pipeline

3. Key Results & Efficiency Metrics

ScamShield: Explainable Real-Time Threat Analysis & Deterministic Risk Scoring Architecture

Abstract

1. Introduction & Problem Statement

2. System Architecture & Tech Stack

Data Flow Pipeline

3. Threat Analysis Engine & Heuristics

4. Key Performance Metrics & Results

5. Future Roadmap