My obsession is simple: help build the best teams. I direct AI systems to make that real — for how I hire, evaluate talent, and operate at scale. Not demos.
I started in Human Resources and moved deliberately toward AI engineering — not to become a developer, but because the systems I needed to build great teams didn't exist yet. So I direct AI to build them.
Everything here exists because it makes a team's hiring sharper, faster, or more reliable — not because it looks good in a portfolio.
Not another ATS bolted onto a point tool — a full HRIS built where legacy providers show their cracks: real multi-tenant architecture instead of a workspace hack, workflows that adapt per team instead of forcing everyone into one template, and a data model designed from day one to scale a growing team, not just log headcount. The ATS below is one module of it.
Built from scratch around what running a real hiring pipeline actually requires: full per-role pipeline customization, multi-tenant data isolation, and a dense, information-first interface designed for daily operational use.
→ Try the live demoA tool for a question I ask on every project: who should do what, given what this specific challenge demands? Load a team's soft skills and preferences, describe the project ahead, and it proposes role assignments — optimizing for fit and for people's happiness as two separate, visible dials, not one hidden average. Runs entirely in the browser: nothing typed in ever leaves the visitor's machine. Free for any HR team to use.
→ Use it freeAn operational framework for running autonomous AI agents safely in production: scoped permission boundaries, a kill-switch, versioned decision logs, rollback strategy, and staged promotion gates before any agent runs unattended — because the tools behind a team deserve the same rigor as the team itself.
→ Fork it, replicate itHiring, or want to see how any of this actually works? I'll walk you through it.
Contact me →This is what actually goes into building a great team — the operational skill set behind everything above.
5+ years recruiting IT profiles end-to-end — sourcing, technical screening, interview loops, offers, and onboarding. ~110 recruitment processes in 2025 alone, 200+ interviews conducted, 45 hires delivered across technical, leadership, and business roles for 10+ product and consulting teams. I know how to evaluate a technical profile I don't personally code — through structured screening, the right interviewers, and reading signal from how a candidate reasons, not just keywords on a CV.
Designing operational frameworks for autonomous AI agents: permission scoping, kill-switches, versioned decision logs, rollback strategy, staged promotion gates, and cross-provider model fallback so agent work stays safe running unattended.
I don't write code by hand. I direct AI systems to build production software through precise technical specification, staged builds, and rigorous, evidence-based verification — I know exactly what "done" looks like before I delegate it.
Evaluation loops that check AI output before it ships, plus cost/risk-aware model selection — fallback ladders and escalation rules instead of trusting a single model blindly.
Beyond recruiting: onboarding, employee engagement, and day-to-day HR operations experience shapes how I direct AI systems — I know what actually changes a hiring or people team's daily workflow, not just what looks good in a demo.
Automated workflows (Power Automate, Microsoft connectors) already running inside a real hiring operation: email triggers, candidate follow-ups, and recruitment-data traceability across the pipeline.
Building working fluency in Power BI and HR analytics to ground AI-directed automation in real operational numbers, not assumptions.
I've watched good teams get built by accident and bad ones get built on purpose — by whoever happened to be understaffed that quarter, not by anyone who actually thought about it. That gap is what I care about closing. Everything else on this page is downstream of that.