Bhushan Shah
Training LLMs | Reinforcement Learning | Deep Learning | Machine Learning
I'm a Master's student in Computer Science at Stony Brook University, specializing in Large Language Models and Reinforcement Learning. My work focuses on understanding the complete lifecycle of LLM development, from initial training to production deployment.
Currently, I'm learning to combine all stages involved in building production-ready LLMs—including pretraining, fine-tuning, reward modeling, deployment strategies, and monitoring systems. My goal is to develop efficient, interpretable, and scalable AI systems that can be reliably deployed in real-world applications.
Work Experience
AI/ML Intern
May 2026 – Aug 2026
- •Built three multi-agent LLM pipelines (debate, moderator, majority-voting) to classify harm intent of abusive accounts across 17-category taxonomy, scoring 180K+ accounts and extending detection beyond binary classification to adversary objective
- •Explored taxonomy-discovery pipelines and showed LLM-generated taxonomies were unstable; motivated fixed taxonomy approach combined with account-level behavior signals
- •Produced the first quantified breakdown of why accounts are abusive across 1M+ unrestricted accounts, enabling enforcement prioritization by scam type
- •Delivered labeled output that reduced manual review effort, supplied training data for distilling smaller open-source models, and became foundation for scammer-persona agent
LLMsAI SafetyLLM Agents
Project Intern
IBM
Sep 2024 – March 2025
- •Curated 2M+ food products labeled dataset from OpenFoodFacts with nutrition, ingredient, and health scores
- •Benchmarked GPT-4o, Mistral Large 2, Llama 3.1 405B, and Gemini Flash 2; identified GPT-4o and Mistral Large 2 as top performers for score prediction task
- •Built Generative AI-powered application allowing users to search food product scores via product name, URL, or image input
- •Optimized inference pipeline through caching, improving latency 10× (2s → 0.2s) for production deployment
- •Developed and integrated LLM inference and function-calling pipelines with backend microservices using FastAPI
WatsonXLLMsAI Applications
Tech Stack
Python
PyTorch
TensorFlow
Verl
TRL
Lighteval
Liger Kernel
LangChain
LangGraph
Scikit-learn
NLTK
Pandas
NumPy
Seaborn
Matplotlib
OpenCV
OpenAI
Hugging Face
Gym
React
TypeScript
Next.js
Docker
AWS