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