Detecting fraudulent job postings in real time with NLP Machine Learning
Online recruitment scams trick thousands of job seekers with realistic-looking postings designed to harvest personal data or advance fees. We built Fake Job Identifier—an NLP machine-learning platform and FastAPI microservice that scores job descriptions, salary anomalies and employer metadata in under 400ms with 98.4% precision.
How we solved Fake Job Identifier AI's core product & operational challenges
Every architectural decision was tied to a measurable speed, revenue or operational accuracy target.
Scam job listings mimic legitimate corporate descriptions, making simple keyword blocklists ineffective.
We trained an ensemble NLP pipeline combining transformer embeddings with structured metadata features (domain age, salary-to-role variance, missing company profile signals).
Users and job-board moderators needed to understand *why* a listing was flagged, not just see a black-box score.
We built an Explainable AI (XAI) breakdown that highlights the exact red-flag phrases and missing employer attributes on every scan.
Fake Job Identifier AI
- Client / Sector
- HRTech & Recruitment Security Initiative
- Target Region
- Global / Cloud SaaS
- Delivery Timeline
- 6 Weeks
- Domain
- AI & Machine Learning
Inside the Fake Job Identifier AI build
Click each architectural layer to inspect the features, workflows and engineering deliverables we shipped.
NLP Text & Linguistic Feature Pipeline
Tokenizes and evaluates job title, requirements, benefits and contact instructions against scam patterns.
Explainable NLP Classification Engine
Combines natural language processing with structured metadata rules to deliver transparent, human-auditable fraud scores.
“RedixCode delivered a production-ready NLP classifier that doesn't just flag suspicious job posts—it explains the exact linguistic and metadata signals behind every alert in under half a second.”
Fake Job Identifier AI questions, answered
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