I train the LLMs on the market's leading models.

Scientist. Engineer. Problem-solver.

I started as a scientist. Lab work at Rutgers, validated protocols, nutritional and mineral analysis — the kind of work where precision isn't optional. That precision is what got me pulled into AI training early: I wasn't just writing prompts, I was quickly promoted to reviewing and correcting the work of other researchers and engineers, because I could see what was wrong before anyone else could.

That pattern — being the one who catches what's broken — is the same reason I went to Columbia Engineering to learn machine learning, neural networks, and transformers from the inside instead of using them as a black box. It's also why small and mid-sized businesses come to me: not because they need another AI vendor, but because they've usually already hired the wrong one, and need someone who can actually diagnose what's broken and fix it.

Same instinct, applied at every scale.