Lead AI Engineer

Airwallex; Singapore; On-site; SGD 180,000 - 300,000; Full time

Job summary

Airwallex is seeking an experienced software leader to guide AI, backend, and frontend engineers in building scalable agent-based systems in Singapore. You will shape architecture for orchestration, retrieval, tool integrations, observability, and evaluation, delivering production-grade AI features from discovery to deployment.

You will collaborate with Product, Design, Data, and Security to translate business needs into secure experiences, champion authentication and human-in-the-loop controls,

Qualifications

• 8+ years software engineering experience with 2+ years leading or managing engineers.
• Deep experience designing and operating scalable agentic architecture and backend distributed systems.
• Deep understanding of retrieval, model integration, agent workflows, evaluation, and observability engineering.
• Disciplined approach to evaluation, monitoring, incident management, and operational readiness for AI systems.
• Deep understanding of secure software design, authentication, authorization, data protection, and abuse prevention.

Responsibilities

• Manage and develop AI, backend, and frontend engineers with clear expectations and technical mentorship.
• Define and evolve architecture for agent orchestration, retrieval, tool integrations, observability, and evaluation systems.
• Lead delivery of production-grade AI features from discovery through launch and continuous improvement.
• Build repeatable evaluation practices for task success, safety, and regressions; define launch criteria and monitoring.
• Drive engineering excellence across service design, APIs, data pipelines, reliability, testing, and incident response.
• Work with Product, Design, Data, and Security to translate business needs into secure experiences.
• Champion authentication, authorization, permission checks, and human-in-the-loop patterns for agent actions and regulatory compliance.
• Create reusable agent platforms, developer tooling, and quality frameworks for safe, high-quality AI delivery.

Skills

Mentorship

AI systems

Backend architecture

Observability

Security

Regulatory compliance

Tools

APIs

Distributed systems

Retrieval

Evaluation tooling

Job description

What you'd do:

• Manage and develop AI, backend, and frontend engineers with clear expectations and technical mentorship.
• Define and evolve architecture for agent orchestration, retrieval, tool integrations, observability, and evaluation systems.
• Lead delivery of production-grade AI features from discovery through launch and continuous improvement.
• Build repeatable evaluation practices for task success, safety, and regressions; define launch criteria and monitoring.
• Drive engineering excellence across service design, APIs, data pipelines, reliability, testing, and incident response.
• Work with Product, Design, Data, and Security to translate business needs into secure experiences.
• Champion authentication, authorization, permission checks, and human-in-the-loop patterns for agent actions and regulatory compliance.
• Create reusable agent platforms, developer tooling, and quality frameworks for safe, high-quality AI delivery.

What they want:

• 8+ years software engineering experience with 2+ years leading or managing engineers.
• Deep experience designing and operating scalable agentic architecture and backend distributed systems.
• Deep understanding of retrieval, model integration, agent workflows, evaluation, and observability engineering.
• Disciplined approach to evaluation, monitoring, incident management, and operational readiness for AI systems.
• Deep understanding of secure software design, authentication, authorization, data protection, and abuse prevention.

8+ years software engineering experience with 2+ years leading or managing engineers.

Deep experience designing and operating scalable agentic architecture and backend distributed systems.

Deep understanding of retrieval, model integration, agent workflows, evaluation, and observability engineering.

Disciplined approach to evaluation, monitoring, incident management, and operational readiness for AI systems.

Deep understanding of secure software design, authentication, authorization, data protection, and abuse prevention.