Data & Machine Learning Engineer - Transaction Monitoring & AML
Omits Inc.
About the role
Company Description
Omits Financial & Technology Inc. is a licensed financial technology company specializing in secure cross-border payment solutions, multi-currency foreign exchange (FX) services, and digital wallet functionality.
We enable fast, safe, and transparent money transfers for individuals and businesses globally, supported by robust, automated KYC/AML compliance systems and a strong regulatory framework.
About the Role
We are building a next-generation cross-border payments platform and are seeking an engineer to design and implement our end-to-end transaction monitoring and financial crime detection system.
This is not a traditional machine learning role. You will be responsible for building a production-grade transaction monitoring engine that combines:
- Rules-based detection (regulatory foundation)
- Real-time risk scoring
- Behavioral analytics and machine learning (layered on top)
The system will be:
- Real-time and highly scalable
- Fully explainable and audit-ready
- Modular and capable of operating as a standalone platform
You will work closely with Engineering and Compliance to translate AML/CFT requirements into a robust, regulator-grade system.
Key Responsibilities
1. Transaction Monitoring Engine
- Design and implement the core monitoring system for both real-time and batch transaction analysis
- Develop a rules-based detection framework, including:
- Velocity monitoring
- Structuring detection
- High-risk geography controls
- New account and onboarding risk patterns
- Ensure all rules are configurable, version-controlled, and fully auditable
2. Real-Time Risk Scoring
- Build a real-time risk scoring engine that evaluates:
- Customer profile
- Transaction context
- Behavioral patterns
- Deliver sub-second decisioning to support transaction processing
3. Data & Feature Infrastructure
- Design and implement pipelines for:
- Event ingestion (transactions, customer actions, external signals)
- Feature engineering (velocity metrics, behavioral aggregates)
- Maintain both real-time and historical data layers for monitoring and analytics
4. Machine Learning (Augmentation Layer)
- Develop models for:
- Anomaly detection
- Behavioral profiling
- Risk signal enhancement
- Focus on:
- Explainability (feature importance, reason codes)
- Reduction of false positives
- Integrate ML on top of rules, not as a replacement
5. System Integration
- Integrate the monitoring engine with:
- Core transaction systems
- Wallet and ledger infrastructure
- KYC and sanctions screening systems
- External risk and data providers
- Ensure full data traceability and consistency across all components
6. Alerting & Case Support
- Build alert generation logic based on defined risk thresholds
- Structure outputs for compliance workflows, including:
- Alert generation
- Case investigation support
- Regulatory reporting readiness (e.g., STR/SAR)
7. Monitoring & Optimization
- Implement:
- Rule performance monitoring
- Model monitoring and drift detection
- Alert quality tracking (false positives vs. true positives)
- Continuously improve detection accuracy and operational efficiency
Required Qualifications
- 3+ years experience in:
- Backend engineering, data engineering, or machine learning
- Experience in fintech, payments, fraud, or AML is strongly preferred
- Strong proficiency in:
- Python
- SQL
- Working with transactional and time-series data
- Experience building:
- Real-time or event-driven systems
- Data pipelines and feature computation systems
- Understanding of:
- AML/CFT concepts (sanctions, PEP, structuring, velocity monitoring, etc.)
- Risk-based transaction monitoring frameworks
- Proven experience deploying systems in production environments
Nice to Have
- Experience building fraud or AML monitoring systems
- Familiarity with:
- Kafka or event streaming systems
- Feature stores
- Graph/network analysis (entity linking, money movement patterns)
- Experience working in regulated environments or supporting audits
What We’re Looking For
- Strong systems thinker (not just model-focused)
- Ability to translate compliance requirements into scalable technical systems
- Attention to detail, especially around auditability and explanability
- Ability to build modular, production-grade infrastructure from scratch
Why This Role Matters
You will be building a core piece of infrastructure that directly protects the platform, enables regulatory compliance, and supports long-term growth.
How to Apply
Please send your application to info@omitsfx.com and include details on:
- Relevant projects or systems you’ve built
- Links to GitHub or portfolio (if available)
Contract Type
3 months
Pay
₦600,000.00 per month
Work Location
Remote
Requirements
- Python
- SQL
- Working with transactional and time-series data
- Experience building: Real-time or event-driven systems, Data pipelines and feature computation systems
- Understanding of: AML/CFT concepts (sanctions, PEP, structuring, velocity monitoring, etc.), Risk-based transaction monitoring frameworks
- Proven experience deploying systems in production environments
Responsibilities
- Design and implement the core monitoring system for both real-time and batch transaction analysis
- Develop a rules-based detection framework, including: Velocity monitoring, Structuring detection, High-risk geography controls, New account and onboarding risk patterns
- Ensure all rules are configurable, version-controlled, and fully auditable
- Build a real-time risk scoring engine that evaluates: Customer profile, Transaction context, Behavioral patterns
- Deliver sub-second decisioning to support transaction processing
- Design and implement pipelines for: Event ingestion (transactions, customer actions, external signals), Feature engineering (velocity metrics, behavioral aggregates)
- Maintain both real-time and historical data layers for monitoring and analytics
- Develop models for: Anomaly detection, Behavioral profiling, Risk signal enhancement
- Focus on: Explainability (feature importance, reason codes), Reduction of false positives
- Integrate ML on top of rules, not as a replacement
- Integrate the monitoring engine with: Core transaction systems, Wallet and ledger infrastructure, KYC and sanctions screening systems, External risk and data providers
- Ensure full data traceability and consistency across all components
- Build alert generation logic based on defined risk thresholds
- Structure outputs for compliance workflows, including: Alert generation, Case investigation support, Regulatory reporting readiness (e.g., STR/SAR)
- Implement: Rule performance monitoring, Model monitoring and drift detection, Alert quality tracking (false positives vs. true positives)
- Continuously improve detection accuracy and operational efficiency
Skills
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