Full-Stack AI Engineer
Summary
The Full-Stack AI Engineer will design and deploy custom AI-assisted tools to automate business workflows while building and maintaining production-grade software across the full stack. The role requires a product-minded approach to engineering, focusing on system reliability and cross-functional collaboration.
Core Responsibilities
Workflow Optimization & AI Automation
Map and analyze existing business workflows to isolate manual, high-friction tasks.
Architect, build, and deploy custom AI-assisted tools that automate repetitive tasks and maximize team leverage.
Work directly with internal stakeholders to drive seamless onboarding and real-world adoption of the tools you build.
Full-Lifecycle Product Engineering
Ship production-grade software across the entire stack—from UI components to database schemas and API integrations.
Own features from initial discovery and technical scoping through to deployment, monitoring, and iteration.
Function as a product-minded engineer, actively proposing solutions and architectural improvements rather than waiting for ticket specs.
Production Quality & AI Ownership
Utilize modern AI development tools (copilots, LLM APIs, synthetic workflows) as force multipliers while maintaining rigorous engineering standards.
Review, test, and debug all AI-generated code to ensure long-term stability, security, and maintainability in production.
Take full end-to-end accountability for system reliability, code performance, and data integrity.
Cross-Functional Collaboration
Operate effectively within a globally distributed, asynchronous engineering team.
Translate complex technical trade-offs into plain language for non-technical users and business leaders.
Background
- Computer Science degree (undergraduate or master’s)
- Graduate level, ideally with 1–2 years of hands-on commercial experience
Chan Chee Meng
07C3069/R1110620