Software Fraud Engineer

GoMining is an international crypto company reshaping how Bitcoin mining works through its Liquid Bitcoin Hashrate (LBH) protocol, making mining accessible, transparent, and profitable. The company develops Digital Miners backed by real hashrate generating daily BTC rewards, the LBH protocol enabling ownership and trading of digital hashrate in DeFi, and the Play-to-Earn strategy game Miner Wars integrated with the Web3 ecosystem. Their ecosystem includes proprietary data centers, the GOMINING token, and operates on Ethereum, BNB Chain, and TON with over 3.7 million users worldwide.

• 4+ years of software engineering experience.
• Experience building fraud prevention, risk, payment, banking, fintech, or security systems.
• Strong backend development experience with 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.

Fraud Detection:

• 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.