About

I’m an AI Engineer with a strong foundation in Data Science, and I love building intelligent systems from data ingestion to deployed agents. My recent project focuses on LLM agents, retrieval architectures (Qdrant + Elasticsearch), and containerized deployments with Docker.

My path into technology was not linear. I studied Public Health but discovered a love for statistics. After working four years as a pricing analyst and completing a Master’s in Data Science, I realized what I enjoyed most was transforming messy data into meaningful insights.

Graduate school taught me modeling techniques, but it didn’t teach me how to build end-to-end products. That realization pushed me toward engineering: understanding ingestion, pipelines, storage, retrieval, reasoning, and deployment as a unified system.

Discovering agentic systems, beyond RAG, that can retrieve and take action was the turning point. I began studying LLM fundamentals before committing to mastering this technology by enrolling in Alexey Grigorev’s AI Bootcamp, where I learned how to build production-ready AI applications.

My Data Science background still plays a big role in how I think. Beyond modeling, I’m comfortable creating data collection pipelines using tools like Selenium, BeautifulSoup, and requests. For me, good AI systems start with good data, and I like understanding every layer of the pipeline.

I’m driven by curiosity, structured learning, and a desire to build AI tools that are genuinely useful. I enjoy projects that combine multiple layers—data ingestion, embeddings, retrieval, reasoning, logging, and deployment—into one cohesive, well-engineered product.

I’m currently looking for roles where I can contribute to LLM engineering, agent systems, retrieval architecture. If you’re working on intelligent automation, agentic systems, or AI-powered workflows, I’d love to connect.