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Nagaraju Kanubuddi
Mid-level AI/ML Engineer specializing in fraud detection, recommender systems, and forecasting
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ML engineer/data scientist who built and deployed a real-time fraud detection platform at Citi on AWS SageMaker, processing 3M+ daily transactions and improving fraud response by 28%. Combines unsupervised anomaly detection (autoencoders) with ensemble models (XGBoost/Random Forest) plus Airflow/Step Functions orchestration, drift monitoring, and explainability (SHAP) to keep models reliable and compliant in production.
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Key Strengths
- Built and deployed real-time fraud detection system processing 3M+ transactions/day
- Reduced fraud response time by 28%
- Hybrid modeling approach (unsupervised autoencoders + XGBoost/Random Forest ensembles) for highly imbalanced, evolving fraud patterns
- Designed low-latency production pipeline with optimized autoscaling
- Production-grade orchestration of end-to-end ML workflows (Airflow, Step Functions) including drift-based retraining
- Strong evaluation discipline (offline replay testing, drift monitoring, recall-focused validation) preventing pre-deploy recall drop
- Effective cross-functional delivery with risk/compliance using explainability (SHAP) and dashboards to drive adoption
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