I build AI systems that actually work in production. Instead of just putting a wrapper around a prompt, I focus on the backend architecture required to make AI reliable, observable, and genuinely useful for a business.
My day-to-day stack usually involves Python, FastAPI, and multi-agent frameworks like LangGraph and LangChain. I spend my time figuring out how to make LLMs reason across complex data, call the right APIs, and integrate smoothly into existing workflows using RAG. The biggest challenge in AI right now is dependability, which is why I focus so heavily on context retrieval, safety guardrails, and system maintenance.
Because my background is in data science with an MS in Data Science and Analytics from Georgia State University, I approach AI with an engineering and evaluation mindset. I don't just care if a model generates a good response; I need to know if the entire system is verifiable and delivering measurable value. You can see this in my recent work on the Autonomous AI Data Analyst Platform, where I handled everything from the initial multi-agent orchestration down to deployment and observability.
I’m always happy to chat with other engineers, researchers, and founders who are out there building practical AI products and solving tough architecture problems.