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Senior AML Modeling Engineer (Payments & Crypto)

Coins

Any Hybrid permanent

Posted: April 28, 2026

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Quick Summary

Develop and optimize AML risk models for anti-money laundering, including designing transaction monitoring strategies and analyzing on-chain data to track complex money laundering patterns, with expertise in machine learning and rule-based engines.

Job Description

Job Description

We are looking for an experienced AML Modeling Expert to build and enhance anti-money laundering models across fiat payment and crypto trading scenarios. This role will leverage on-chain and transactional data to identify complex money laundering patterns and strengthen the platform’s overall risk control and compliance capabilities.


Responsibilities:
• Develop and optimize AML risk models (rule-based engines + machine learning)

• Design transaction monitoring strategies (e.g., anomaly detection, transaction structuring, fund flow analysis)

• Analyze on-chain data to track fund movements and identify high-risk address behaviors

• Build user risk scoring systems integrating KYC, transactional, and blockchain data

• Continuously improve model performance, reduce false positives, and enhance investigation efficiency


Requirements:
• 5+ years of experience in AML / risk modeling (mandatory)

• Proven end-to-end experience in deploying AML models (e.g., transaction monitoring, risk scoring, investigation support)

• Strong proficiency in Python and SQL for data analysis and modeling

• Solid understanding of common AML typologies (e.g., layering, smurfing, cash-out)

• Hands-on experience with machine learning models (e.g., XGBoost, LightGBM)


Preferred Qualifications:
• Experience in crypto / blockchain analytics

• Familiarity with on-chain analytics tools (e.g., Chainalysis, Elliptic)

• Background in AML within payments, banking, or crypto industries

• Experience with graph analytics or real-time risk systems


Goals:
• Enhance detection capabilities for complex money laundering activities

• Reduce false positives and improve investigation efficiency

• Proactively identify high-risk fund flows

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