Lead AI Engineer

The role

You will lead the technical design, execution, and people leadership for Kai, an AI assistant for complex financial work. You will build reliable production systems using LLM and agent capabilities and manage AI, backend, and frontend engineers.

What you will do

• Lead technical design, execution, and people leadership for Kai
• Turn LLM and agent capabilities into reliable, observable, and safe production systems
• Set technical direction for agent orchestration, context, retrieval, and tool integrations
• Work with Product, Design, Data, Security, Infrastructure, and domain teams
• Manage, coach, and develop AI, backend, and frontend engineers
• Raise the engineering bar through hiring, feedback, and technical mentorship
• Define and evolve technical architecture for identity, access control, and observability
• Lead delivery of production-grade AI features from discovery to launch
• Establish product and model success metrics
• Build repeatable, versioned evaluation practices

Required qualifications

• 8+ years of software engineering experience
• 2+ years leading, managing, or formally mentoring engineers
• Track record of building strong teams and delivering complex products end to end
• Deep experience designing and operating scalable agentic architecture, backend systems, or distributed systems
• Understanding of engineering realities of retrieval, model integration, agent workflows, evaluation, observability, and changing model behaviour
• Disciplined approach to evaluation, monitoring, incident management, performance, testing, and operational readiness
• Understanding of secure software design including authentication, authorisation, data protection, auditability, and abuse prevention
• Ability to define and evolve technical architecture across agent orchestration, context and retrieval, tool integrations, identity and access control, observability, and evaluation systems
• Ability to lead the delivery of production-grade AI features from discovery through launch and iteration
• Ability to establish product and model success metrics, instrument customer journeys, and use real-world feedback to improve AI usefulness and trustworthiness
• Ability to build repeatable, versioned evaluation practices for task success, groundedness, tool use, safety, and domain-specific failure modes
• Ability to define launch criteria, staged-rollout and rollback mechanisms, and monitoring

Nice to have

• Comfortable owning the roadmap yourself
• Hold yourself to a high standard even when no one is watching
• Comfortable with ambiguity
• Communicate clearly and directly across cultures
• Simplify complexity

About the company

Airwallex is an AI-native financial operating system for a real-time, intelligent economy. It provides financial infrastructure for global commerce and is co-headquartered in San Francisco and Singapore.