Where things stand right now
A living view of what I'm building, learning, and looking for - updated as it changes, not written once and left to go stale.
Last updated: July 2026
// building
Querify - an AI-powered BI platform that turns CSV/XLSX uploads into dashboards, forecasts, and natural-language queries. React frontend with Supabase, FastAPI backend on AWS, Firebase for authentication, and Groq powering the natural-language layer.
Right now I'm working through deployment-stage authentication and CORS issues between the Firebase-authenticated frontend and the FastAPI backend - the kind of unglamorous problem that doesn't show up in a demo video but has to get solved before anything ships.
AWS// deepening
I'm currently deepening my AI engineering skills - the common thread across everything I'm reading and testing right now is the systems thinking behind reliable RAG and agentic applications, not just getting a demo to work, but getting it to work consistently. I'm also working through IBM's AI Engineering Professional Certificate on Coursera to put some structure around what I've picked up on the job.
IBM AI Engineering Professional Certificate on Coursera - in progress.
The retrieval and chaining primitives I already lean on for RAG work.
Exploring its state model for multi-step agent flows that plain chains handle poorly.
Testing multi-agent task delegation - where it helps, and where it just adds overhead.
Structured ways to measure whether a RAG system is actually reliable, not just plausible.
Testing all of this directly against Querify and the RAG projects, not in isolation.
// looking for
A fresher Data Scientist or AI Engineer role where I can keep pairing strong software foundations with applied machine learning - a team that ships things, not just prototypes them.
I'm most drawn to roles with real applied AI work: RAG systems, computer vision, or ML pipelines that touch a live product, rather than research disconnected from users.