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Balakrishna Mylapilli
Mid-level AIML Engineer specializing in production ML and MLOps
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ML practitioner who built a production customer risk scoring system to replace slow manual approvals, owning the full pipeline from feature engineering and XGBoost training to deploying a Dockerized FastAPI prediction service. Emphasizes reliability and business-aligned evaluation (recall/ROC-AUC, threshold tuning, drift monitoring) and is comfortable translating model decisions into stakeholder metrics like conversion rate (experience at EasyBee AI).
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Key Strengths
- Built and deployed an end-to-end production risk scoring system (data prep → modeling → API deployment)
- Production deployment experience with Dockerized FastAPI real-time inference service
- Strong handling of imbalanced classification (stratified CV, class weighting, SMOTE experimentation, threshold tuning)
- Metric-driven model evaluation aligned to business goals (recall/ROC-AUC over accuracy)
- Experiment tracking and reproducibility using MLflow
- Reliability mindset: edge-case testing, A/B testing, post-deploy monitoring for drift
- Effective communication with non-technical stakeholders by translating model changes into business impact (e.g., conversion vs risk tradeoffs)
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