Software Fraud Engineer

GoMining; Georgia; Hybrid; USD 120,000 - 170,000; Full time

Benefits offered by this job

Professional growth: courses and 100%英

Remote or hybrid format with flexible,

Job summary

GoMining is hiring a Software Fraud Engineer to design and build a next-generation fraud prevention platform. You will develop real-time fraud detection systems, risk engines, and automated decision-making services that protect users and the business.

The role focuses on building fraud prevention systems rather than manual fraud operations, with responsibilities spanning payments, wallets, authentication, and user activity detection.

Qualifications

• 4+ years of software engineering experience.

Responsibilities

• Design and develop fraud detection systems for payments, wallets, authentication, and user activity.
• Detect account takeover, payment fraud, bonus abuse, multi-accounting, and other abuse scenarios.
• Build real-time fraud pipelines and event processing systems.
• Develop automated fraud prevention mechanisms.
• Build and improve fraud rules, velocity controls, and dynamic risk scoring.
• Design decision engines for transaction approval, review, or rejection.
• Continuously optimize fraud detection accuracy while minimizing false positives.
• Improve fraud models using behavioral and transactional data.
• Develop scalable backend services supporting fraud prevention.
• Build APIs and internal tooling for risk evaluation.
• Optimize latency for real-time fraud decisions.
• Collaborate closely with Product, Payments, Data, and Platform teams.

Skills

Go

Java

Kotlin

Python

Distributed systems

SQL

Payment flows

Real-time decision engines

English

Tools

Kafka

ClickHouse

Redis

Elasticsearch

Job description

Responsibilities

Fraud Detection

We're looking for a Software Fraud Engineer to design and build our next-generation fraud prevention platform. You'll develop real-time fraud detection systems, risk engines, and automated decision-making services that protect both our users and business. This is an engineering role focused on building fraud prevention systems — not a manual fraud operations position.

• Design and develop fraud detection systems for payments, wallets, authentication, and user activity
• Detect account takeover, payment fraud, bonus abuse, multi-accounting, and other abuse scenarios
• Build real-time fraud pipelines and event processing systems
• Develop automated fraud prevention mechanisms

Risk Engine

• Build and improve fraud rules, velocity controls, and dynamic risk scoring
• Design decision engines for transaction approval, review, or rejection
• Continuously optimize fraud detection accuracy while minimizing false positives
• Improve fraud models using behavioral and transactional data

Data & Analytics

• Analyze transaction patterns, user behavior, device intelligence, and risk signals
• Work with large-scale event streams and transactional datasets
• Create internal investigation and monitoring tools for Fraud, Support, and Compliance teams
• Investigate emerging fraud patterns and rapidly deploy countermeasures

Engineering

• Develop scalable backend services supporting fraud prevention
• Build APIs and internal tooling for risk evaluation
• Optimize latency for real-time fraud decisions
• Collaborate closely with Product, Payments, Data, and Platform teams

Requirements

• 4+ years of software engineering experience
• Experience building fraud prevention, risk, payment, banking, fintech, or security systems
• Strong backend development experience (Go, Java, Kotlin, Python, or similar)
• Experience working with distributed systems and event-driven architectures
• Strong SQL skills and experience with large datasets
• Understanding of payment flows, authentication, and transactional systems
• Experience designing real-time decision engines
• Strong analytical and problem-solving skills
• Fluent English

Nice to have

• Experience in crypto, Web3, or blockchain
• Experience with Kafka, ClickHouse, Redis, Elasticsearch, or similar technologies
• Experience with machine learning models for fraud detection
• Knowledge of AML, KYC, or payment risk systems
• Experience with device fingerprinting, behavioral analytics, or identity verification

Benefits

• Professional growth: support for courses, conferences, and English learning (up to 100% coverage)
• Work-life fit: remote or hybrid format with flexible hours across international teams
• Paid leave: up to 20 vacation days + 8 company holidays + 5 personal days per year
• Recognition programs: structured performance reviews and team awards
• Team culture: retreats in international locations (for example, company apartments in Cyprus)