Head of Engineering (Software Architect, AI)

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• AI-native culture with enterprise AI tools
• Flexible hybrid work, focused on outcomes
• Small team, high ownership

Mô tả công việc

WHAT WE ARE

Zeya Labs is a small AI-native engineering team based in Ho Chi Minh City. We are the central technology delivery engine for a diversified group spanning financial services, F&B, retail, hospitality, and real estate.

We build shared platform utilities, run AI pilots across group entities, and set the engineering standard for how software gets built. We are not a consultancy. We do not hand off blueprints. We build, run, and own what we ship.

We are early. The org is being stood up now. If you need things to be settled before you can do your best work, this probably isn't the right move.

WHAT THIS ROLE IS

You own the engineering function end to end — architecture, code quality, technical standards, and the production systems Zeya Labs runs on. Early on, that means being hands-on: writing code, making architecture calls, setting the technical bar yourself. As the team grows, your job shifts from doing the work to building the team that does it — hiring engineers, developing them, and making sure the standard holds without you touching every line.

You also own the handoff from our Product & AI function. That team builds fast — front-end code, AI-integrated interfaces, functional prototypes that get a problem to working software quickly. Your job is to take what they build and make it production-grade: hardened, scalable, maintainable. But this role is wider than a hardening desk. You're also running engineering initiatives of your own, and where AI product work and core engineering overlap, you're expected to have a real point of view and get involved directly — not wait for something to be thrown over the wall.

Concretely, the Head of Engineering is responsible for three things:

• Technical ownership. Architecture, code quality, engineering standards, and the production infrastructure the company runs on. You set the bar and you're hands-on enough early to prove it works.
• Hardening and shipping what Product & AI builds. Taking prototypes and functional builds from that team and turning them into systems that can be trusted in production — without slowing the pace that makes the AI Product Builder model work.
• Building the engineering team. Hiring the engineers Zeya Labs needs, developing them, and scaling your own role from builder to leader as the team grows.

Yêu cầu công việc

WHO WE'RE LOOKING FOR

You've been a strong hands-on engineer and you're ready — or already there — to lead. You can architect a system, write production code, and review someone else's work with a sharp eye, but you don't need to be the one typing forever. You know the difference between "I can do this myself" and "I can build a team that does this well," and you're motivated by the second one without having lost the first.

You have genuine, delivered experience shipping AI-integrated systems into production — not pilots, not proof-of-concepts, not a certification. This is a hard filter. You've owned something that had to actually hold up under real usage, and you understand where AI tooling helps, where it creates fragility, and how to harden something that was built fast into something that lasts. If your AI exposure is coursework, side projects, or demos that never saw production traffic, this isn't the right fit.

You're comfortable working next to a product-and-AI function that moves quickly and builds rough — your job isn't to gatekeep their pace, it's to make sure what ships doesn't fall over. You have opinions about engineering standards and you'll defend them, but you're pragmatic about a startup's need for speed over ceremony.

A few things that matter to us:

• You default to building, not managing process for its own sake — even as you scale into leadership.
• You can go from "the only engineer in the room" to "the person hiring the room" without losing your technical edge.
• You have strong instincts for what production-ready AI infrastructure actually looks like, versus a demo that will break under real load.
• You're comfortable with ambiguity in what gets built next, but intolerant of ambiguity in who owns quality and uptime.

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