AI Engineering (Vietnam) - English Speaking - Remote

Mô tả công việc
Mức độ phù hợp và xếp hạng của bạn so với ứng viên khác như thế nào?
We are building the software hotels use to serve, know and win back their guests. Our mission: evolve Japanese omotenashi through technology.
IFHA (Inbound-First Hospitality Alliance): our alliance of 400+ hotels, ryokans and resorts across Japan, plus 15+ facilities in Vietnam.
Guest-facing SaaS: GEM (guest engagement), MenyGo (mobile ordering) and Smartphone Concierge - 1.6M+ cumulative system users.
AI visibility: AEO (answer-engine optimisation) audits, AI Profile - a machine-readable hotel data directory built for AI systems to cite - and the AI Learner Dashboard.
IT consulting: DX strategy, system design and offshore development in Vietnam with JP/EN bridge engineering.
Why now. Hotels have lost their guests three times: to OTAs (booking), to Google (discovery) and to COVID (the human touch went digital). The fourth shift is AI becoming the interface that decides who gets found at all. We are rebuilding company around it - tooling that lets one consultant serve many hotels, and an operations function run on agents.
The role
AI Engineering is the CIAO's (Chief AI Officer's) right hand and the builder-in-chief of our agent and automation stack. You will turn the CIAO's roadmap into shipped production systems - largely by directing AI coding agents rather than by hiring a large engineering team.
Just as important as shipping: you own the standard those agents work to. The instruction layer - [protected info] / [protected info], sub-agent definitions, skills, prompt libraries, review gates and evals - is a product in this company, and it is yours to build, version and keep healthy as models and tools change.
This is a deeply hands-on technical role, but we are not hiring output - we are hiring leverage. Your own code matters less than the difference you make to everyone else's: the engineer who ships twice as much because your instruction files are good, the consultant who stops doing a task by hand, the non-engineer who can safely run an agent. You will be judged on team throughput and quality, not personal commit count. That makes written and spoken communication a core technical skill here, not a soft extra: most of your leverage is delivered as writing that other people (and agents) act on.
Reports to: Chief AI Officer (CIAO), Japan HQ
Works with: Product & AI team, Ops, Sales, and client-facing consultants
Type: Full-time, senior
Location: Vietnam
What you'll do
1. Build with AI coding agents, daily. Design, prompt, review and ship production code - internal tools, data pipelines, integrations and client-facing products - using AI coding agents as your primary engineering workforce. We use Claude Code today and stay deliberately tool-agnostic: Codex, Cursor, Gemini CLI, Copilot and whatever ships next are all fair game if they do the job better.
2. Own and maintain the agent instruction layer. Author and continuously maintain [protected info] / [protected info] and equivalent instruction files, repo conventions, sub-agent definitions, skills, slash commands, MCP tool wiring and scheduled jobs - so that any agent, on any tool, produces work to the same standard. Treat these as versioned, reviewed, tested assets, not scratch notes.
3. Set and enforce the engineering standard for AI-built code. Define how we write prompts, tests, evals, code review and guardrails; define what an agent may do unsupervised, what needs a human gate, and what is never automated. Keep a measurable bar (review pass rate, escaped defects, rework) rather than a vibe.
4. Run the agent workflow as a system. Design the handoff pipeline from requirement → scoped agent task → PR → review → deploy. Keep context hygiene, repo safety (pre-checks, reversibility, rollback), and destructive-operation policy explicit and followed.
5. Scale the consultant. Build the tooling that lets one our consultant serve many hotels - audits, reports, data structuring and delivery automation.
6. Support AI Profile and AEO products. Build and maintain the data pipelines, schema and feeds behind our hotel data products.
7. Run the CIAO's execution. Translate strategy into scoped projects, estimates and weekly delivery; report progress, cost and risk back to the CIAO.
8. Measure and control cost. Track model and token spend across tools, pick the right model and tool for each job, and keep unit economics healthy.
9. Multiply the team. Train engineers, consultants and non-engineers to work with AI tools safely and well; turn repeated work into reusable skills, templates and plugins; run short reviews and working sessions that raise the whole team's level instead of routing every hard task through you. Aim to make yourself removable from the critical path.
10. Communicate across borders. Write the specs, decision records, weekly updates and risk/blocker reports that keep Japan HQ, the Vietnam team and client-facing staff aligned - clearly enough that a non-engineer can act on them and a senior engineer can argue with them. Say plainly what is decided, what is open, and what needs the CIAO's call.
11. Keep us current. Evaluate new agents, models, MCP servers and workflow tools on a regular cadence; recommend adopt/trial/hold with evidence, migration cost and risk - not hype.