From eCommerce Solutions Engineering to Applied GenAI

In January 2026 I was caught in the BigCommerce layoff wave. I had spent more than three and a half years across pre-sales, sales, and partner Solutions Engineering: technical discovery with merchants, turning messy multi-stakeholder requirements into something you can scope, and feeding field signal back into the platform roadmap. Product work, even if the title said otherwise.

When it happened, I set myself a different goal than just jumping to the next seat. Every earlier layoff had landed me somewhere that already pulled me in. This time the market was clear: I needed to learn AI for real, hands-on.

I started where I already had traction — freelance projects in my local community, ecommerce stores, people asking me to build. Claude for day-to-day work. I also tried Chinese models (Kimi, MiniMax) to code cheaper; they hallucinated a lot. Early lesson: AI is a tool, not magic. You have to evaluate, iterate, and keep judgment in the loop.

I was not starting from zero in Product. Marketing, BDR, a decade of Agile (Scrum Master → PM → Senior PM), UX, business analysis, analytics — then BigCommerce. The long way around every seat at the table. What was missing was the AI layer on top of that same craft.

Since early 2026 I have been adding it by building:

• NodoLab — AI-powered products end-to-end: a multi-tenant CRM in production, WhatsApp ordering on live catalogs, LLM workflows with evaluation loops that control output variability.
• Aijolot — AI Project Manager: driving AI adoption and automation with distributed teams.
• EB2B — technical direction for a B2B directory and membership platform.

The layoff was not the plan. It was the push to stop watching AI from the bench — and to sit where Product and Solutions meet, speaking both the business and the model.