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