Machine Learning Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Build and deploy machine learning models for fraud protection by analyzing banking transactions and detecting anomalies. Focus on graph-based transactional analysis, uncovering complex relationships in financial data such as cryptocurrency flows, and integrating models into the Fraud Protection platform. Research state-of-the-art fraud and anomaly detection techniques, develop production-ready Python (Go a plus), and communicate findings. Collaborate on ETL pipelines and MLOps-aligned development practices.

Location: Kazakhstan

Workplace: Remote

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Identify and formalize scenarios covered by the transactional antifraud system, especially in the AML domain.
•Research cutting-edge ML models and techniques for fraud and anomaly detection in transactional data.
•Build classic, graph-based, and neural network ML models and integrate them into the Fraud Protection platform.
•Develop production-ready source code primarily in Python (with Go as a plus).
•Document and present research findings.

Pay and Benefits

Perks:Health Insurance

Key Requirements

•Strong foundation in transactional data analysis and fraud/anomaly detection use cases.
•Hands-on experience with ML frameworks such as PyTorch or Keras/TensorFlow.
•Practical knowledge of classical ML and neural networks, including Graph ML concepts (node embeddings, link prediction, GNNs).
•Experience designing and building ETL pipelines.
•Ability to develop production-ready code (primarily in Python).

Experience:Fraud detectionAMLGraph analyticsTransactional data

Skills:DocumentationPresentationResearch mindsetExperimentation

Tech Stack:PythonPandasScikit-learnPyTorchNetworkXPyGNeo4jAWS NeptuneElasticsearchCassandraClickHouseGitDockerGoAWS

Company Brief

Group IB

Website

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Group IB

1 month ago

Machine Learning Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Location: Kazakhstan

Workplace: Remote

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Mid level

Key Responsibilities

•Identify and formalize scenarios covered by the transactional antifraud system, especially in the AML domain.
•Research cutting-edge ML models and techniques for fraud and anomaly detection in transactional data.
•Build classic, graph-based, and neural network ML models and integrate them into the Fraud Protection platform.
•Develop production-ready source code primarily in Python (with Go as a plus).
•Document and present research findings.

Pay and Benefits

Perks:Health Insurance

Key Requirements

•Strong foundation in transactional data analysis and fraud/anomaly detection use cases.
•Hands-on experience with ML frameworks such as PyTorch or Keras/TensorFlow.
•Practical knowledge of classical ML and neural networks, including Graph ML concepts (node embeddings, link prediction, GNNs).
•Experience designing and building ETL pipelines.
•Ability to develop production-ready code (primarily in Python).

Experience:Fraud detectionAMLGraph analyticsTransactional data

Skills:DocumentationPresentationResearch mindsetExperimentation

Tech Stack:PythonPandasScikit-learnPyTorchNetworkXPyGNeo4jAWS NeptuneElasticsearchCassandraClickHouseGitDockerGoAWS

Company Brief

Group IB

Website