AI Product Engineer & AI Solution Engineer

An AI capability on its own, a model, a retrieval pipeline, an agent, isn't a product. It becomes one when it's wired into a real application: an interface people actually use, a backend that handles concurrent users and real data, and infrastructure that stays up. Companies often have the AI piece figured out, or outsourced, but struggle to get it integrated into something a team can operate day-to-day, especially when the person who prototyped the model isn't a product engineer.

I work both ends of the stack on the same project. On the AI side, that means choosing the right approach for the actual problem, a RAG pipeline, an agentic workflow, or a fine-tuned model, and making sure it's testable and explainable to non-technical stakeholders rather than a black box. On the product side, I build FastAPI backends with real-time streaming over SSE, Next.js frontends with live dashboards, PostgreSQL or MongoDB for persistence, and role-based access control for systems with multiple user types. Everything runs on AWS EC2 with S3 for storage, so the AI component and the application around it are deployed and versioned together rather than handed off between teams.

A deployed application, not a demo, with a backend, a frontend, a database, and the AI logic all shipping as one system. This is the architecture behind my AI Sales Intelligence Platform: a LangGraph agentic backend streaming SSE responses, pre-aggregated KPI pipelines, and a Next.js conversational dashboard delivering natural-language analytics over a 50M+ record warehouse, and behind my Visitor Management System, a full enterprise application with a Next.js frontend, MongoDB backend, and AWS S3 media handling, deployed end-to-end on EC2.