Full-Stack AI Engineer
On-siteHo Chi Minh City, Ho Chi Minh City (HCMC), Vietnam
Ho Chi Minh City, Ho Chi Minh City (HCMC), VietnamOn-siteFull TimeStartup
Full TimeStartup
Job Summary
Own end-to-end delivery across frontend, backend, data, and infrastructure layers, leveraging AI tooling like LLMs and agentic workflows as a daily productivity lever. Design system-level solutions with documented trade-offs, prototype AI workflows, and build production-grade observability to catch failure modes AI misses. Monitor metrics to validate output, respond to incidents autonomously, and encode learnings into team artifacts that reduce bus factor. Decompose ambiguous requirements before coding, ensuring designs survive beyond your involvement.
Required Qualifications
- 3+ years in software engineering with demonstrated delivery across more than one layer of the stack (frontend, backend, data, or infrastructure)
- Hands-on experience shipping production AI features — LLM integration, agentic workflows, or AI-assisted systems at scale
- Proficiency in modern web and backend frameworks
- experience with LLM APIs, vector databases, or AI orchestration tooling
- Strong English communication skills (spoken and written — professional fluency required)
- High adaptability and comfort operating in fast-paced, ambiguous environments
Desired Qualifications
- Startup or tech background is a strong plus
- You use AI tooling daily and can describe concretely how it changes your output — not just what tools you use, but how your workflows have evolved and what teammates have adopted
- You decompose ambiguous problems before reaching for a solution; root-cause analysis is a reflex, not a last resort
- You connect frontend, backend, data, and infrastructure concerns — you do not hand off when the problem crosses layers
- You encode what you learn; your contributions outlast the feature you are building
- AI Engineer/Architect — Ships end-to-end AI-powered software across BE + FE + agents + data with Claude; owns the full delivery, not just a layer
- Product Architect — Designs at workflow and system level; documents trade-offs and alternatives that survive past the sprint and get referenced by others
- Domain Expert — Builds deep understanding of the product domain (credit decisioning, eKYC, fraud, or core engineering disciplines like security or observability); encodes it into artifacts teammates use
- AI Operator — Monitors production AI systems above quality threshold; catches drift before it becomes an incident; acts on metrics, not assumptions
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