A manager who codes, debugs and jointly deploys — and grows the team that does.

Because this role is the part of my job I love most, with the constraint I can't engineer around removed. What lights me up is leading a team in the room with customers — coding, debugging and deploying together until AI actually runs in their environment — and growing the people who do it. As a product engineering venture firm CIO I do that with a product engineering venture firm's reach. At Google Cloud the same work comes with frontier Gemini models, the Vertex AI platform, and direct access to DeepMind's engineering minds behind it. I'd rather build and grow that field team from inside the platform than keep consulting from outside it.

Fair — and I won't overstate it. But the primitives transfer directly: I've staffed and grown customer-facing engineering teams, owned end-to-end delivery outcomes, set code standards and architectural benchmarks, run technical hiring, and coached engineers into bigger roles — two alumni now lead at organisations in Norway. And my engineers did forward-deployed work in everything but name: an embedded pod shipped 0→production for Norway's Frontgo; four years alongside SkyTracks' CTO in Canada. What's new is Google's scale and the GTM context, and I'd close that by learning the existing playbooks before changing anything. What I bring that's rare is a decade of deployment scar tissue and a founder's accountability for both the technical and the business outcome.

I'll be honest rather than retrofit my résumé: the AI I've shipped into client production has been Claude-led, and that candour matters to me. But Gemini is genuinely second in my stack, not a gesture — VisaPros runs six parallel agents on Gemini 2.5 Flash Lite with Google Search grounding and was my Google × Kaggle 5-Day AI Agents Intensive capstone; NewScriber renders its dual-host podcasts with Google's Gemini TTS multi-speaker mode (speech-style modifiers, interruption, temporal and contextual continuity, 16-bit PCM stitched via ffmpeg); I work with Google ADK; my production deploys already run on Google Cloud Run. The craft this role needs — context engineering, multi-agent decomposition, evals, integration, governance — is model-agnostic, and I'd ramp the Vertex AI specifics visibly. A field team's credibility with customers comes from honesty about exactly this kind of thing.

Correct, and I won't pretend otherwise. My field of study was Computer Science & Engineering at North South University, and I took the shipped-artifact route from there: nine-plus years of production systems, the Google × Kaggle Agents Intensive capstone, engineering pattern papers, and technical webinars. The preferred qualification proxies for depth in agentic system design — ReAct, self-reflection, state management, tool-calling protocols — and that's exactly what I can demonstrate in running, open-source code rather than a thesis. I'm happy to be tested on it at whatever depth the panel wants.

Player-coach is the only way I've ever worked. I came up as a UX Engineer, grew through Principal Staff SWE into management, and never stopped shipping — the Claude CMS and VisaPros are recent, personal builds, not delegated ones. Standards-setting at ELO was concrete: component libraries, NX monorepos, an OIDC/RBAC backbone every product ships on, CI/CD that delivered 4x faster time-to-market (75% reduction in cycle time). Mentorship the same — code review, pairing on the hard part, stretch ownership with real stakes. That's why my alumni outcomes are real: people I coached now lead at organisations in Norway. The management multiplies the building; it doesn't replace it.

By holding the line the JD itself draws: this team doesn't just consult — it codes, debugs and jointly deploys. I'd partner closely with Sales and Technical Leadership on qualification, deploying specialized experts — MLOps, generative media, agentic systems — against high-value opportunities, because field engineering time is the scarcest resource in the building. But the team's success metric is AI running in the customer's environment at enterprise-grade maturity, not slideware. And every engagement feeds the loop back: field insights become roadmap input for Product and Development, and what works gets codified into internal tools. I've run exactly that tension for a decade as a founder accountable for revenue and what ships.

Yes — Canada is a deliberate, researched target, not a checkbox. Canada's Global Talent Stream is one of the visa paths I've prepared for, and my work has pointed there for years: four years embedded with SkyTracks in Canada alongside their CTO, fractional engagements in Quebec, an alum who pursued graduate study in Canada on my recommendation, and Global Jute Trading's Canada-first export push that I architected. I run on global timezones today and would land in Toronto on local hours from day one.

Straight answer first: I'm not fluent — I'm at CEFR A2 and climbing — so for anything client-facing in French today I'd pair with a native speaker rather than fake it; respect for the customer beats bravado. But the habit itself is the real signal. I've kept a 908-day unbroken daily French streak and I teach A2 grammar in public every week as @FrenchwithFauzul — that's not a language flex, it's evidence of exactly how I close a gap: relentless, daily, in the open, with no audience to perform for. It's the same discipline I'd point at the Vertex AI platform and at every unfamiliar customer domain a forward-deployed team drops into. For a bilingual country with Francophone enterprise customers (Québec especially) it also means genuine cultural respect and a real trajectory toward working in their language — I show up willing to meet people where they are and to keep getting measurably better at it. That adapt-and-learn mentality is the whole job; the French streak just proves I actually live it.