I build applied AI systems, backend platforms, and cloud-native full-stack products.
I am an MS Computer Engineering student at NYU focused on LLM agents, RAG systems, async backends, real-time streaming, and production infrastructure.
AI systems that feel useful in production:
idea -> backend -> workers -> data layer -> frontend -> deployment
> Multi-agent research systems
> AI career intelligence workflows
> Real-time LLM apps with streaming progress
> Backend platforms with FastAPI, Redis, PostgreSQL, and AWS
> Full-stack products with Next.js, TypeScript, and cloud deployment
| Project | What it does | Stack |
|---|---|---|
| Singularity | Multi-agent deep research platform with DAG-style orchestration, hybrid retrieval, streaming progress, and worker pipelines | FastAPI, Redis, PostgreSQL, Qdrant, Next.js, SSE |
| Wand | AI career intelligence platform for job research, resume analysis, profile scoring, and company intelligence workflows | FastAPI, Celery, Redis, SQLAlchemy, LLMs, Next.js |
| Finassistant | AI-powered finance assistant for document and data workflows | Python, LLMs, Backend Systems |
| Snap2Caption | Vision-language captioning system using LLaVA and LoRA | PyTorch, LLaVA, React, MLflow |
| PGDR | OOD robustness method using gradient disagreement reweighting | PyTorch, Research, Waterbirds |
Frontend / Product UX
|
v
API Layer / FastAPI
|
v
Async Workers / Queues
|
v
PostgreSQL + Redis + Vector Stores
|
v
LLM Orchestration / RAG / Agents
|
v
Docker + AWS + CI/CD
- Applied AI products
- LLM agents and RAG systems
- Backend engineering
- Real-time systems with WebSockets and SSE
- Cloud infrastructure and deployment
- Full-stack product development
- ML systems and model evaluation
