I'm a Machine Learning Intern at Kismet in New York, with a passion for building AI-driven solutions. I have experience developing large language model pipelines and integrating multimodal inputs. I've worked on projects like Super-Play and MarketPulse-AI, and hold a Bachelor's degree in Computer Science, currently pursuing my Master's in Computer Engineering. At Kismet, I’ve built real-time hotel data scrapers powered by GPT agents, developed backend attribution systems using FastAPI, and deployed containerized services on Google Cloud. My work spans NLP, semantic search, UTM-based tracking, and agentic task orchestration using LangChain. I'm particularly interested in real-world applications of AI that blend language, automation, and analytics into user-facing tools.

I'm eager to explore AI-first product development, particularly in real-time LLM pipelines, agentic workflows, and attribution modeling. I'm also interested in collaborating on applied NLP, autonomous scraping agents, and scalable ML infra. I’d love to connect with builders focused on multimodal AI, data orchestration, or mission-driven early-stage tools.

At Kismet, I'm currently working on LLM pipelines and integrating multimodal inputs to build smarter, more adaptive systems. I’ve been developing AI-driven solutions through projects like Super-Play, which focuses on natural language processing, and MarketPulse-AI, which involves real-time data analysis and web scraping. Most of my work is in Python, where I focus on building machine learning workflows, handling data ingestion, and integrating models to create functional prototypes for real-world AI applications.