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Hey, I'm Rithvika Tiruveedhula

AI/ML Developer | GenAI Enthusiast

Final-year B.Tech Computer Science and Engineering student at Vellore Institute of Technology(VIT), Chennai with hands-on experience in AI/ML, deep learning, and real-world application development.

+91-9538232897 rithvika.tiruvee@example.com

About Me


Hello! I'm Rithvika T, a passionate AI/ML developer and final-year Computer Science student at VIT Chennai. I specialize in deep learning, computer vision, and building intelligent, real-world AI systems.

With hands-on experience in healthcare diagnostics, fine-grained image similarity, and generative AI models, I enjoy turning data into impactful solutions. My work bridges academic research and industry-driven development, with a strong focus on model explainability, accuracy, and scalability.

I believe in using AI to solve meaningful problems from improving medical diagnostics to optimizing retail analytics and thrive in fast-paced, collaborative environments where innovation meets execution.

Python
TensorFlow/Keras
PyTorch
Deep Learning
Computer Vision
Transfer Learning
NLP (Natural Language Processing)
Scikit-Learn
Flask (for deploying ML models)

Work Experience


Time Period Company Role Experience
Jan, 2025 - Present Tata Consultancy Services AI/ML Intern
  • Developed a Fine-Grained Image Similarity model using CNNs, Vision Transformers, and Triplet Loss.
  • Built a scalable FAISS-based retrieval system for high-precision SKU matching and stock differentiation.
Nov, 2023 - Jan, 2024 Iota Analytics Pvt Ltd Summer Intern (AI/ML/NLP)
  • Built a privacy-preserving NLP pipeline using Hugging Face and custom regex for PII redaction.
  • Implemented a RAG system with FAISS and LLMs for domain-specific Q&A via Flask API
Jun, 2023 - Aug, 2023 Chakralaya Analytics Intern
  • Contributed to a real-time Business Intelligence system for procurement and strategic planning.
  • Created dashboards and KPIs for actionable insights used by CEOs and buyers.

My Projects


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Ship Detection using SAR Imagery for Maritime Vigilance

Built a ResNet50-based model to detect ships in noisy SAR images, optimized with FPN and PSO. Presented at ICDSAAI 2025 for its improved accuracy in cluttered maritime scenes.

Tensorflow OpenCV CNNs
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Lung Disease Classification using Ensemble Transfer Learning and Grad-CAM

Developed an ensemble-based AI model for lung disease detection from chest X-rays, combining five pre-trained CNNs. Integrated Grad-CAM for explainability and deployed the system via Flask for real-time clinical use.

TensorFlow Grad-CAM Flask
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Fine Grained Image Similarity for Apparel

A deep learning-based system for identifying subtle visual differences between similar apparel items using attention mechanisms, attention erasure, and ResNet50 embeddings for retail inventory optimization.

Image Similarity Attention Mechanisms Attention Erasure ResNet50 OpenCV
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RAG with AES Encryption

Developed a secure Retrieval-Augmented Generation (RAG) system using Hugging Face LLMs for document-based question answering. Integrated AES encryption to protect sensitive outputs and ensure privacy in local PDF/DOCX processing.

RAG LLMs AES Encryption
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Arrhythmia Detection using ECG Signals

Developed a 1D CNN model to classify cardiac arrhythmias from ECG data using the MIT-BIH dataset, with wavelet-based denoising and class balancing via SMOTE. Achieved accurate classification across heartbeat types, forming a strong foundation for real-time health monitoring systems.

1D CNN ECG Signal Processing SMOTE
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Chatbot using PyTorch and NLP

Developed a chatbot using PyTorch and NLP techniques to provide natural language responses to user queries. The chatbot uses a pre-trained model to generate responses and can be trained on new data to improve its performance.

PyTorch Seq2Seq Modeling

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