Location: Gurgaon / Remote (Hybrid / Flexible Work Policy)
Employment Type: Full time
Experience Required: 3–5 Years
Industry-Standard Salary Range: 18 LPA – 32 LPA
About Us & Our Mission
In the fast-paced ecosystem of digital B2B fintech settlement rails and cross-border payments, financial fraud attempts are increasingly sophisticated and executed in milliseconds. Protecting enterprise ledger systems and merchant accounts requires real-time anomaly detection, sophisticated graph analysis, and instantaneous risk scoring.
Our engineering culture values proactive threat mitigation, high-performance data processing, and uncompromised system reliability. If you want to build intelligent risk engines that protect multi-million-dollar financial networks from malicious actors, you will find your professional home with us.
The Role & Impact
We are seeking a sharp, data-driven Fintech Fraud & Risk Analytics Engineer to design, build, and deploy real-time fraud detection and risk scoring systems. In this role,
you will analyze massive transaction streams, build behavioral profiling models, and implement automated rules and machine learning classifiers that flag suspicious activities before settlement occurs. You will work at the critical intersection of data science, backend engineering, and financial security.
Key Responsibilities & Daily Expectations
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Risk Engine Development: Design and scale real-time transaction screening microservices and rule evaluation engines using Python, FastAPI, and asynchronous message queues.
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Feature Engineering: Build and maintain low-latency feature stores that compute rolling user velocity, device fingerprinting, and transactional behavioral anomalies on the fly.
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Model Integration: Integrate machine learning classification and anomaly detection models (Isolation Forests, XGBoost, graph-based link analysis) directly into payment processing pipelines.