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I am an AI/ML Engineer with 4 years of experience designing, building, and deploying machine learning solutions across IT services and US banking domains. I have a strong track record across the full ML lifecycle, including data modeling, feature engineering, model deployment, and production monitoring. I work with large-scale structured data and cloud-native ML pipelines to solve business-critical use cases like credit risk, fraud detection, and automation. I enjoy turning complex data problems into practical, measurable improvements for customers and stakeholders.…I am an AI/ML Engineer with 4 years of experience designing, building, and deploying machine learning solutions across IT services and US banking domains. I have a strong track record across the full ML lifecycle, including data modeling, feature engineering, model deployment, and production monitoring. I work with large-scale structured data and cloud-native ML pipelines to solve business-critical use cases like credit risk, fraud detection, and automation. I enjoy turning complex data problems into practical, measurable improvements for customers and stakeholders.

Tharun Kumar Nagipogu

AI Engineer, Data Scientist, AI Developer, +2

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I am an AI/ML Engineer with 4 years of experience designing, building, and deploying machine learning solutions across IT services and US banking domains. I have a strong track record across the full ML lifecycle, including data modeling, feature engineering, model deployment, and production monitoring. I work with large-scale structured data and cloud-native ML pipelines to solve business-critical use cases like credit risk, fraud detection, and automation. I enjoy turning complex data problems into practical, measurable improvements for customers and stakeholders.…I am an AI/ML Engineer with 4 years of experience designing, building, and deploying machine learning solutions across IT services and US banking domains. I have a strong track record across the full ML lifecycle, including data modeling, feature engineering, model deployment, and production monitoring. I work with large-scale structured data and cloud-native ML pipelines to solve business-critical use cases like credit risk, fraud detection, and automation. I enjoy turning complex data problems into practical, measurable improvements for customers and stakeholders.

Available to hire

I am an AI/ML Engineer with 4 years of experience designing, building, and deploying machine learning solutions across IT services and US banking domains. I have a strong track record across the full ML lifecycle, including data modeling, feature engineering, model deployment, and production monitoring.

I work with large-scale structured data and cloud-native ML pipelines to solve business-critical use cases like credit risk, fraud detection, and automation. I enjoy turning complex data problems into practical, measurable improvements for customers and stakeholders.

Skills

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Work Experience

AI/ML Engineer at Capital One

August 1, 2024 - Present

Built a credit default prediction model using XGBoost in Python, trained on three years of transaction, repayment, and bureau data, improving risk-based loan approval precision by 18% compared to legacy scoring models. Created customer behavior features using pandas and NumPy, including spending velocity, utilization trends, and delinquency patterns, increasing model discrimination and lifting AUC from 0.71 to 0.83. Developed a LightGBM-based fraud detection model optimized for real-time scoring, enabling card transactions to be evaluated within 200 ms during authorization. Implemented SHAP explainability to surface key drivers behind risk and fraud scores, supporting internal audit reviews and reducing model validation turnaround time by 30%. Deployed models as Dockerized REST APIs, integrating them into internal decisioning systems that process millions of daily transactions without impacting latency. Set up model performance and drift monitoring using MLflow, allowing early detectio

Associate Software Engineer – AI/ML at HCLTech

January 1, 2021 - July 1, 2023

Prepared and analyzed large-scale retail and enterprise datasets using Python (pandas), supporting forecasting and automation use cases across multiple client engagements. Built a sales demand forecasting solution using Facebook Prophet, incorporating seasonality and promotional effects to reduce inventory stock-outs by 22% for a retail client. Developed an automated IT service ticket classification model using Logistic Regression, improving incident prioritization accuracy and reducing manual triage effort by 35%. Created reusable data preprocessing pipelines with scikit-learn, handling missing data, categorical encoding, and scaling across datasets exceeding 10 million records. Implemented a customer churn prediction model using Random Forest, identifying early warning signals from customer behavior and improving targeted retention campaign effectiveness by 17%. Performed model tuning with GridSearchCV, improving generalization performance and ensuring consistent results across diffe