Passionate about leveraging cutting-edge AI to automate workflows and solve complex problems. With a strong foundation in LLM agents and multimodal models, I specialize in building intelligent systems that drive efficiency and innovation. Currently pursuing an M.S. in Computer Science at NYU with a perfect 4.0 GPA, I'm dedicated to exploring the intersection of AI, automation, and real-world applications.

Let's connect to discuss how AI-driven solutions can transform industries.

• Programming Languages: Python, JavaScript, TypeScript, Java, SQL, Bash
• Front-End Development: HTML, CSS, React, Vue.js
• Back-End Development: Node.js, Express.js, Flask, GraphQL
• Databases: PostgreSQL, MongoDB, SQLite, Redis
• Cloud & DevOps: Docker, Kubernetes, AWS (S3, Lambda, OpenSearch, API Gateway, DynamoDB, EKS, CloudFormation, EventBridge), Azure AI, Heroku
• AI/ML: PyTorch, Scikit-learn, NLTK, Langchain, LlamaIndex, CrewAI, Autogen
• Data Analysis: Pandas, NumPy, Matplotlib, Seaborn, Power BI
• Other Tools: Web Scraping (Selenium, BeautifulSoup), Prompt Engineering, Twilio, Assembly AI, yt-dlp

• New York University
  Master of Science in Computer Science
  Sept. 2024 – Present | New York, NY
  GPA: 4.0/4.0
  Courses: Cloud Computing, Deep Learning, Information Visualization

• Indian Institute of Technology Madras
  B.Sc in Data Science and Applications
  Jan. 2021 – Apr. 2024 | Chennai, Tamil Nadu

• Savitribai Phule Pune University
  B.E. in Computer Engineering
  Aug. 2019 – May 2023 | Pune, Maharashtra
  GPA: 3.7/4.0

Jan. 2024 – July 2024

• Developed an AI-driven phone calling agent using Twilio, Flask, Assembly AI, GPT-4, and RAG
• Engineered a scalable multi-lingual synthetic data generation system achieving 90% accuracy
• Analyzed 3.2M Kissan Call Center interactions, improving chatbot performance by 10%
• Implemented LLM-driven SQL query generation for real-time market data integration

Mar. 2023 – Aug. 2023

• Developed Python solution for parsing government job advertisements with 80% accuracy
• Implemented vector embedding techniques improving recommendation accuracy by 25%
• Automated data extraction processes reducing manual labor for a team of 10

Dec. 2024

• Developed synthetic dataset generation pipeline for fine-tuning and knowledge removal
• Engineered framework to fine-tune and unlearn outdated parameters in Codellama-7B
• Achieved superior semantic alignment (CODEBERT: 0.9517) using LoRA and gradient ascent-based selective forgetting

Sept-Dec. 2024

• Built cloud-native tool for automated security scanning of GitHub repositories
• Integrated fine-tuned Qwen 2.5 Coder 7B model trained on 300,000 entries
• Developed optimized version for Snapdragon X Elite laptops using LM Studio

Feel free to reach out if you want to collaborate on projects or discuss AI innovations. Located in Brooklyn, New York.

"Transforming industries through AI innovation and intelligent automation"