Software Fraud Engineer

About the role

GoMining is looking for a Software Fraud Engineer to design and build its next-generation fraud prevention platform. The engineer develops real-time fraud detection systems, risk engines and automated decision-making services that protect users and the business. This is an engineering role focused on building fraud prevention systems, not a manual fraud operations position.

Key responsibilities

• Fraud detection: design and develop fraud detection systems for payments, wallets, authentication and user activity, covering account takeover, payment fraud, bonus abuse, multi-accounting and other abuse scenarios
• Build real-time fraud pipelines and event processing systems, and develop automated fraud prevention mechanisms
• Risk engine: build and improve fraud rules, velocity controls and dynamic risk scoring, and design decision engines for transaction approval, review or rejection
• Optimize fraud detection accuracy while minimizing false positives, improving models with behavioral and transactional data
• Data and analytics: analyze transaction patterns, user behavior and device intelligence, work with large-scale event streams and datasets, and build internal investigation and monitoring tools for Fraud, Support and Compliance teams
• Investigate emerging fraud patterns and deploy countermeasures quickly
• Engineering: build scalable backend services, APIs and internal tooling for risk evaluation, optimize latency for real-time decisions, and collaborate with Product, Payments, Data and Platform teams

Required profile

• 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 with distributed systems and event-driven architectures
• Understanding of payment flows, authentication and transactional systems
• Experience designing real-time decision engines
• Fluent English

Required skills

• Strong SQL skills and experience with large datasets
• Nice to have: Kafka, ClickHouse, Redis, Elasticsearch
• Nice to have: machine learning models for fraud detection, AML/KYC knowledge, device fingerprinting, behavioral analytics, identity verification

What we offer

• Professional growth: support for courses, conferences and English learning (up to 100% coverage)
• Remote or hybrid format with flexible hours across international teams
• Paid leave: up to 20 vacation days, 8 company holidays and 5 personal days per year
• Performance reviews, team awards and retreats in international locations