Full-Stack AI Engineer

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