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HemaSri Perumalla
Mid-level AI/ML Engineer specializing in fraud detection and healthcare predictive analytics
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About
ML/AI engineer with production experience in high-scale banking fraud detection at Truist, building an end-to-end pipeline (Airflow/AWS Glue/Snowflake, PyTorch/sklearn) with automated retraining and Kubernetes-based deployment; delivered measurable gains (22% fewer false positives, 15% higher recall) and reduced manual ops ~40%. Also partnered with clinicians at Kellton to deploy an LLM system for summarizing/classifying clinical notes, improving review time and decision speed.
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Key Strengths
- Built and deployed production fraud detection ML system at a bank
- Reduced false positives by 22% and improved recall by 15%
- Designed scalable end-to-end data/ML pipeline (Airflow + AWS Glue + Snowflake)
- Implemented automated retraining and deployment (SageMaker + Lambda)
- Containerized and deployed microservices with Docker and Kubernetes
- Established monitoring dashboards and operationalized model monitoring (Power BI)
- Improved workflow reliability with SLAs/retries and data quality checks (Great Expectations)
- Used rigorous evaluation practices (backtesting, adversarial cases, shadow/canary, A/B tests)
- Translated non-technical clinician needs into an LLM solution and iterated via prototypes
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