MS in AI candidate at the New Jersey Institute of Technology with a focus on building and benchmarking production-grade Generative AI systems. My work spans the full development lifecycle, from implementing cutting-edge research to engineering scalable, high-performance AI applications.
- GenAI & LLMs: RAG Architecture, LLM Fine-Tuning (PEFT/LoRA), Autonomous Agents (ReAct), Vector Databases (FAISS, ChromaDB), LangChain
- ML & Data Science: Scikit-Learn, Pandas, NumPy, XAI (SHAP, LIME), Computer Vision
- Engineering & DevOps: Git, REST APIs (Flask/FastAPI), Streamlit, SQL
| Project | Description | Key Technologies |
|---|---|---|
| Project Chimera | A resilient, multimodal autonomous AI agent with sandboxed tool-use (file system, code interpreter, web) and a human-in-the-loop fallback. | Python, vLLM, Transformers, ChromaDB, Florence-2, Whisper |
| MedQuery-RAG | An end-to-end medical RAG system powered by vLLM for 5-10x inference throughput, featuring an intent classifier and cross-encoder re-ranker. | Python, vLLM, RAG, FAISS, Streamlit, Cross-Encoders |
| Project CLAIRE | A benchmark study on continual learning, empirically demonstrating the trade-off between factual retention and linguistic coherence in LLMs. | Python, PyTorch, PEFT/LoRA, Llama-3.1, LLM-as-a-Judge |
| Self-Align for Go | An implementation of the Self-Align data generation pipeline to create a high-quality, validated instruction-response dataset for the Go language. | Python, StarCoder2, vLLM, Go, Data Pipelines |