• 📧 How to reach me: [email protected]

• 🖋 Know about my experiences: My Portfolio

I’m a 19-year-old who chose a self-directed path after 12th grade to dive into learning. Over the past 1.5 years, I’ve focused on Data Science, specializing in Machine Learning, Analytics, and an in-depth exploration of Generative AI. Three months ago, I secured a paid NLP Data Science internship, which I successfully completed in December. I’m a flexible and passionate learner who loves books and startups, and I’m now seeking full-time opportunities to contribute and grow further.

• Indian Institute of Technology Madras — Bachelor’s Degree in Data Science & AI
• Data Science Masters Courses, Physics Wallah

• Programming Languages and Libraries: Python (Pandas, Numpy, Matplotlib, Seaborn, Scikit-Learn), JS (React.js)
• Frameworks: Flask, TensorFlow, PyTorch, LangChain, LangSmith, LangGraph
• Machine Learning Algorithms: Linear Models, Tree-based Models, Clustering Models, Association Models
• Deep Learning: ANN, CNN, RNN (LSTM), TensorFlow & PyTorch
• Applications: Anomaly Detection, LLM Fine-Tuning (QLoRA, LoRA), SOTA RAG Methods, AI Agents
• MLOps: MLflow, ZenML, DagsHub, Git/GitHub, DVC, Docker, Evidently AI
• Cloud Technologies: AWS (SageMaker, S3, Lambda)

• Data Science Intern @ EduGorilla
  • Objective: Developing a robust LLM trained on educational books & datasets
  • Research & Model Selection: Conducted extensive research to select a Hugging Face model for fine-tuning educational content using PEFT, saving 40% resources
  • Benchmarking: Incorporated a small benchmark model to validate and compare performance effectively
• AI/ML Hackathon Runner-Up | IITM
  • Achievement: Secured 2nd place by building a robust ensemble model combining GPT-2, BERT variants, and others with advanced techniques like weighted averaging and label separation
  • Performance: Trained on 50k samples, achieving 99.9% accuracy on validation data and 98.3% accuracy with a macro F1 score of 95.13 on imbalanced test data
• GitHub Repository Assistant (Advanced RAG)
  • Objective: Simplify documentation and assist in extracting required code snippets from repositories
• Homestays Price Prediction | Internship Task
  • Objective: Developed a machine learning model to predict house prices using detailed property information with efficient MLOps implementation