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Bhupal Reddy Thippireddy

AI & Machine Learning Engineer (Internships) • India • b**************@gmail.com • +91*******319

Professional Summary

AI & Machine Learning-focused software engineer with hands-on internship experience building and deploying ML and deep learning models. Strong Python, SQL, and data engineering skills with practical experience in TensorFlow, Scikit-learn, CNNs, feature engineering, model evaluation, and prototype deployment using Flask/Joblib. Able to translate business problems into scalable ML solutions, produce clear visualizations, and document reproducible pipelines for production handoff.

Technical Skills

Programming Languages: Python,R,C
Web Technologies: REST API
Frameworks and Libraries: TensorFlow,Scikit-learn,XGBoost,OpenCV,Pandas,NumPy,Flask,Matplotlib,Seaborn
Databases: SQL,MySQL
Cloud and DevOps: Joblib,Jupyter Notebook
Data and Analytics: Data Preprocessing,Feature Engineering,Supervised Learning,Convolutional Neural Networks,Classification,Regression,Model Evaluation,Cross-Validation,Hyperparameter Tuning,Exploratory Data Analysis
Tools and Methodologies: Git,GitHub

Work Experience

Infotact Solutions
Remote
Data Science and Machine Learning Intern
Dec 2025 – Mar 2026
Tech Stack: Python, Scikit-learn, TensorFlow, Pandas, NumPy, Flask, Joblib, Matplotlib, Seaborn, Git, GitHub, Jupyter Notebook
  • Built and optimized predictive analytics models using Python, Scikit-learn and TensorFlow to address business forecasting needs and prototype proof-of-concepts.
  • Designed and implemented end-to-end ML pipelines including data cleaning, feature engineering, model training, validation and serialization to enable repeatable experiments.
  • Applied cross-validation and hyperparameter tuning to improve model generalization and reduce overfitting during development cycles.
  • Serialized trained models with Joblib and packaged prototype REST endpoints using Flask to demonstrate model inference and enable stakeholder testing.
  • Produced analytical reports and visualizations with Matplotlib and Seaborn to communicate model findings, feature importance and performance comparisons to non-technical stakeholders.
  • Collaborated remotely using Git/GitHub, documented model evaluation metrics and handoff notes to support transition from prototype to production engineering teams.
Elevate Labs
Remote
Artificial Intelligence Intern
Apr 2025 – May 2025
Tech Stack: TensorFlow, Convolutional Neural Networks, Python, Pandas, NumPy, Data Augmentation, Cross-Validation, Jupyter Notebook
  • Developed a CNN-based forest fire detection system from satellite imagery using TensorFlow, achieving over 85% classification accuracy on validation data.
  • Built robust data preprocessing and augmentation pipelines to address class imbalance and variability in satellite image conditions, improving model robustness.
  • Conducted hyperparameter tuning, implemented early stopping and used cross-validation to optimize model performance and control overfitting.
  • Implemented training and evaluation workflows in TensorFlow, producing clear performance metrics and visualizations (accuracy, loss curves) to analyze model behavior.
  • Optimized model architecture and data input pipelines for efficient batch inference to support near-real-time detection scenarios.
  • Documented dataset preparation, model architecture choices and reproducible Jupyter notebooks to ensure knowledge transfer and future model iterations.

Projects

Netflix Movies and TV Shows Analysis
Tools Used: Python, Pandas, NumPy, Scikit-learn, XGBoost, Matplotlib
  • Processed and analyzed a dataset of Netflix titles to identify content trends and engagement patterns through exploratory data analysis (EDA).
  • Performed feature engineering and data preprocessing to prepare inputs for predictive modeling.
  • Built predictive models using Random Forest and XGBoost to improve content-related prediction accuracy and evaluated models using standard metrics.
  • Produced visualizations to summarize insights and support data-driven conclusions for content strategy.
IoT-Based Predictive Maintenance System
Tools Used: Python, Pandas, NumPy, Scikit-learn, Flask, Joblib
  • Developed an ML model to predict machine failures using operational sensor data and predictive analytics techniques.
  • Performed feature scaling, data transformation and model optimization to improve prediction accuracy.
  • Implemented a Flask-based prediction API and used Joblib for model serialization to demonstrate end-to-end predictive maintenance workflow.
Face Recognition Based Attendance System
Tools Used: Python, OpenCV, MySQL, NumPy
  • Built an automated face recognition attendance system using OpenCV for real-time face detection and recognition.
  • Integrated a MySQL database for attendance tracking and implemented report generation for monitoring.
  • Created analytical attendance reports and visualizations to support administrative review.

Education

Parul University, Vadodara, Gujarat
B.Tech in Computer Science and Engineering (Artificial Intelligence) • Vadodara, Gujarat, India • 2022 – 2026
Narayana Junior College
Intermediate (Class XII) • 2020 – 2022
A.P Model School
Secondary School (Class X) • 2020

Certifications

Database and SQL — Infosys Springboard
Data Science — Cisco Networking Academy
AI and Machine Learning — Elevate Labs

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