Engineer for Fraud Detection System

Hiring: Senior Backend / ML Engineer

Ongoing Contract — Fraud Detection & Document Intelligence Platform

Project: FraudX
Type: Long-term contract (with expansion potential)
Focus: Document fraud detection, OCR intelligence, ML readiness
Stage: Live production system – final accuracy + expansion phase

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OVERVIEW

I’m building FraudX, a production-grade document fraud detection platform used to analyze:
• Paystubs
• Proof of income
• (Next phase) Proof of residence
• (Next phase) Driver licenses & IDs
• (Next phase) Bank statements & financial docs

The system is already live, handling real documents, with a working backend, UI, OCR pipeline, and fraud rules engine.

I am looking for a senior engineer to help:
• Improve extraction accuracy
• Harden document parsing
• Expand to new document types
• Prepare the system for long-term ML training
• Continue building new fraud detection modules over time

This is not a short-term gig.
If we work well together, this will become ongoing work.


WHAT IS ALREADY BUILT (LIVE IN PRODUCTION)

Infrastructure
• Ubuntu VPS (DigitalOcean)
• Gunicorn + Uvicorn
• systemd
• Nginx + SSL
• PostgreSQL (asyncpg)
• Fully deployed backend

Core Platform
• FastAPI backend
• Role-based access (Admin / Dealer / Guest)
• Immutable scan storage
• File hashing
• OCR pipeline
• Admin dashboard
• Scan audit trail

OCR & Extraction
• Google Vision OCR
• Google Document AI
• AWS Textract
• OCR normalization layer
• OCR fallback handling
• GPT used as non-authoritative assist
• Multi-provider OCR support

Fraud System
• Deterministic fraud rules engine
• PASS / CAUTION / FAIL output
• Explainable flags
• Non-ML decisioning (ML is advisory only)
• Admin labeling + review system

ML Readiness
• Training labels stored
• Feature storage in JSONB
• Shadow ML scoring
• Future LightGBM / XGBoost pipeline planned

1. Paystub Extraction (Top Priority)

Current state:
• OCR works
• Text exists
• Some extraction works
• But line-item parsing is incomplete

Needs:
• Earnings table extraction
• Deductions parsing
• Multi-line pay components
• Hourly vs salary normalization
• YTD vs current matching
• Employer/employee cleanup
• Reliable structured output

This is the core issue affecting accuracy.

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2. OCR Consensus & Normalization

We already run:
• Google Vision
• Google DocAI
• AWS Textract
• GPT assist

We need:
• Cross-engine reconciliation
• Confidence scoring
• Conflict resolution
• Smart merging of results

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3. Future Document Types (Next Phase)

Planned additions:
• Proof of residence
• Driver licenses
• Bank statements
• Employment letters

Same pipeline, new extractors.

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4. ML Readiness (Ongoing)

Already have:
• Real + fake documents
• Labeled fraud examples
• Admin review system

Need:
• Feature engineering
• Consistent schema
• Model-friendly extraction
• Training pipeline readiness

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IDEAL CANDIDATE

You should be comfortable with:

Python (FastAPI preferred)
OCR pipelines
Document parsing
PDF / image processing
Structured data extraction
PostgreSQL / JSONB
Async systems
Production debugging

Big Plus If You Have:

Fraud detection experience
Fintech / underwriting systems
ML feature engineering
OCR accuracy optimization
PDF forensics
Document templating systems

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WHAT YOU WILL NOT DO

Rebuild frontend
Rewrite the system
Replace architecture
Work on UI styling

This is backend intelligence work, not UI.

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ENGAGEMENT TYPE
• Long-term contract
• Ongoing feature development
• Multiple document types coming
• Flexible hours
• Paid hourly or milestone-based

Rate:
Open to discussion (expecting senior-level rates)
$60–$120/hr depending on experience
or fixed-price per module

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TO APPLY — PLEASE ANSWER:
1. Have you worked with OCR or document parsing before?
2. Have you built systems that extract structured data from PDFs?
3. Have you worked with fraud, fintech, or identity verification?
4. How would you approach paystub parsing?
5. What tools/libraries would you use?
6. Are you available for ongoing work?
7. Your hourly rate or project estimate