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sai Pavan
Mid-level AI/ML Engineer specializing in MLOps, NLP, and real-time ML pipelines
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Built a production, real-time insurance claims document-understanding and fraud-detection pipeline using TensorFlow + fine-tuned BERT, deployed on AWS (SageMaker/Lambda/API Gateway) with automated retraining via MLflow and Jenkins. Addressed noisy documents and latency using augmentation and model distillation (3x faster), cutting claims ops manual review by ~50% and reducing fraudulent payouts.
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Key Strengths
- Built and deployed real-time insurance claims document-understanding + fraud-detection system to production
- Reduced manual claims review effort by ~50%
- Improved inference latency via model distillation (3x faster) while maintaining accuracy
- Strong MLOps/productionization on AWS (SageMaker, Lambda, API Gateway) with automated retraining (MLflow + Jenkins)
- Robust ML orchestration design (Airflow/Step Functions) with retries, idempotency, and state management (S3/DynamoDB)
- Reliability-focused evaluation and rollout practices (unit/E2E tests, shadow testing, A/B tests, drift monitoring)
- Effective cross-functional collaboration with non-technical claims operations via early demos and workflow-driven communication
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