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Founded in 2003 and headquartered in Singapore, Group-IB is a leading creator of cybersecurity technologies to investigate, prevent, and fight digital crime. Combating cybercrime is in the company’s DNA, shaping its technological capabilities to defend businesses, citizens, and support law enforcement operations. Group-IB’s Digital Crime Resistance Centers (DCRCs) are located in the Middle East, Europe, Central Asia, and Asia-Pacific to help critically analyze and promptly mitigate regional and country-specific threats. These mission-critical units help Group-IB strengthen its contribution to global cybercrime prevention and continually expand its threat-hunting capabilities.   ABOUT THE ROLE We are looking for an experienced Machine Learning Engineer (Middle+/Senior) to strengthen our Fraud Protection team within a multinational cybersecurity company. The primary mission of this role is to analyze banking transactions to detect and prevent fraud and to enhance existing fraud detection algorithms. A key focus of the position is graph-based transactional analysis, including identifying complex relationships in financial data such as cryptocurrency flows. You may also contribute to broader graph-related tasks, for example building GraphRAG components for AI-driven agents. YOUR MISSION Identify and formalize scenarios to be covered by the transactional antifraud system, especially in the AML domain. Conduct research on cutting-edge ML models and techniques for fraud and anomaly detection in transactional data. Build ML models (classic, graph-based, neural networks) and integrate them into the Fraud Protection platform. Develop production-ready source code (primarily in Python; Go experience is a plus). Document and present research findings. Tech Stack Python 3.8+ Data analysis & modeling: pandas, scikit-learn, PyTorch Graph libraries/frameworks: NetworkX, PyG, Neo4j, AWS Neptune (any of these) Databases (nice to have) :  Elasticsearch, Cassandra, ClickHouse Git, Docker AWS ecosystem   WHAT WE ARE LOOKING FOR Have a solid foundation in transactional data analysis. Understand neural networks (CNNs, RNNs, etc.), classical ML algorithms, and where to apply them—especially Graph ML (node embeddings, link prediction, GNNs). Have hands-on experience with frameworks such as PyTorch or Keras/TensorFlow. Are proficient in both IDE-based and terminal-based development. Have experience designing and building ETL pipelines. Have a research-oriented mindset and enjoy experimentation.   WHAT SETS YOU APART Strong documentation and presentation skills. Experience with MLOps practices. Experience writing production-ready asynchronous Python code.   WHY CHOOSE GROUP-IB Group-IB is a global leader in cybersecurity technologies that investigate, predict, prevent, and fight digital crime. We help organizations reduce risk and protect trust. Trusted by governments, major industries, and law enforcement, we deliver adversary-focused, predictive threat intelligence and cyber fraud fusion solutions that detect, analyse, and mitigate regional and country-specific digital crimes. Work with real stakes. Group-IB investigates active cybercriminal groups, responds to breaches affecting critical infrastructure, and develops technologies used by law enforcement agencies including INTERPOL, Europol, and Afripol across 60+ countries. We've conducted 1,550+ cybercrime investigations alongside 600+ enterprise customers globally. When you join Group-IB, your work directly disrupts digital crime. Grow your way. Choose your own path: deepen your craft as a technical expert, step into leadership, move across to another team, or relocate to one of our Digital Crime Resistance Centers across the Americas, Europe, the Middle East & Africa, Central Asia, and the Asia-Pacific. Your growth is our growth — Group-IB's expansion acr