We are looking for an experienced Machine Learning Engineer 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.
Tech Stack
Responsibilities
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.
This role is perfect for you if you:
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.
Preferred:
Strong documentation and presentation skills.
Experience with MLOps practices.
Experience writing production-ready asynchronous Python code.
Why choose Group-IB
What else you should know
Sounds like you? Apply now!
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