Role & responsibilities
Design and develop graph-based fraud detection models using graph databases.
Build and optimize graph data models for fraud analytics and risk detection.
Develop and execute graph queries using Cypher and Gremlin.
Work with AWS Neptune and/or Neo4j for graph database implementation.
Build and deploy Graph Neural Network (GNN) models for fraud detection and link analysis.
Analyze complex relationships and patterns to identify fraudulent activities.
Collaborate with data scientists, engineers, and business stakeholders to deliver fraud analytics solutions.
Optimize graph database performance and ensure scalability of fraud detection systems.

Preferred candidate profile
Solid experience in Fraud Modeling and Fraud Analytics.
Hands-on experience with AWS Neptune and/or Neo4j.




Proficiency in Cypher Query Language and Gremlin.
Experience with Graph Neural Networks (GNNs).
Robust understanding of graph data structures, relationship modeling, and network analytics.
Valuable analytical and problem-solving skills.
Excellent communication and stakeholder management skills.
Experience in Financial Services, Banking, or FinTech fraud detection.
Knowledge of Machine Learning and AI techniques for fraud prevention.
Experience working with cloud platforms, preferably AWS.
Familiarity with Python or Java for graph analytics is an added advantage.

📌 Fraud Modeling Graph Analytics Engineer Haryana (India)
🏢 Infinites Hr Services Pune
📍 India