Headline

AI/ML Student | Generative AI | MachineLearning.

About

Computer Science graduate specializing in Artificial Intelligence and Machine Learning, with a passion for building intelligent systems that solve real-world problems. I have hands-on experience developing and deploying machine learning solutions using Python, PyTorch, and modern AI frameworks. My key projects include a Retinal OCT Analyzer that achieved 94% accuracy on a dataset of 84,000+ medical images and a Heart Disease Prediction System built using end-to-end machine learning pipelines. My technical expertise includes Machine Learning, Deep Learning, Python, Java, SQL, Data Structures & Algorithms, PyTorch, and AWS Cloud technologies. I enjoy tackling complex challenges, optimizing model performance, and developing scalable, data-driven applications. I hold the Oracle Generative AI Professional certification and the Amazon Web Services Cloud Practitioner certification. Additionally, I have solved 100+ coding problems on LeetCode to strengthen my problem-solving and software engineering skills. Currently seeking opportunities in AI/ML Engineering, Software Engineering, and Data Science, where I can contribute to impactful projects, continue learning, and grow as a technology professional.

Location

India

Work experience

Generative AI Intern at Prodigy InfoTech, Jun 2026 - Jul 2026 · 2 mos, Hyderabad, Telangana, India, • Currently serving as a Generative AI Intern at Prodigy InfoTech. • Gaining hands-on experience in Generative AI, Large Language Models (LLMs), and AI application development. • Learning and applying prompt engineering techniques to build AI-powered solutions. • Working with Python and modern AI tools/frameworks to develop practical projects. • Exploring real-world applications of Machine Learning and Natural Language Processing (NLP). • Enhancing problem-solving, research, and technical development skills through industry-oriented tasks.

Deep Learning Engineer at Vellore Institute of Technology, Jan 2026 - Apr 2026 · 4 mos, Developed a hybrid ensemble–deep learning framework for credit card fraud detection on highly imbalanced transaction data. Built and evaluated multiple machine learning models including Logistic Regression, Random Forest, SVM, XGBoost, and a custom Deep Neural Network architecture. Applied SMOTE-based oversampling, feature preprocessing, hyperparameter tuning, and model optimization to improve fraud detection performance. Designed a Deep Learning + XGBoost hybrid model achieving 0.77 F1-score, 0.79 PR-AUC, and real-time inference latency suitable for production environments. Implemented the solution using Python, PyTorch, Scikit-learn, XGBoost, Pandas, NumPy, and Machine Learning pipelines. Collaborated with a team member to conduct experimentation, performance analysis, and comparative evaluation on the Kaggle Credit Card Fraud Detection dataset.

Education

Bachelor of Technology, Computer Engineering, Vellore Institute of Technology

INTERMEDIATE, MPC, sasi junior college