Engineering Lead/Manager, Risk

About the role

Airwallex, the AI-native financial operating system used by more than 675,000 businesses, is hiring an Engineering Lead/Manager for its Risk Platform in Singapore. The team builds the decisioning infrastructure that protects customers and revenue at global scale, using streaming pipelines, graph databases, ML models and LLM agents to detect fraud in real time. The role is based in the Singapore office five days a week.

Key responsibilities

• Own the strategy, roadmap and delivery of real-time detection systems and client-facing fraud management products.
• Provide strategic leadership for the risk platform, aligning technical direction and capacity planning with business goals and regulatory requirements.
• Partner with Product, Design, Operations, Commercial, Strategy, Platform, Data, SRE, Security and global engineering teams to improve detection rates, false positives, chargebacks, reliability and cost efficiency.
• Build and scale a high-performing engineering organisation, including managers and senior individual contributors, with career paths and succession plans.
• Guide architecture, review critical code and APIs, and raise the bar on design, code quality, observability and incident response.
• Own system quality across real-time decisioning services, ensuring reliability, scalability, high availability and high throughput.
• Lead talent strategy: recruiting, onboarding, developing engineers and managers, and building diverse teams.

Required profile

• 8+ years of backend engineering experience, including 2+ years in engineering management and ownership of complex, distributed systems in production.
• Experience in risk management or fintech product engineering; a bachelor's degree in Computer Science or a related field.
• Fluency in English; able to work across time zones with a globally distributed team.
• Preferred: leadership across multiple disciplines (frontend, backend, data, machine learning, SRE) with manager-of-managers scope for Director-level candidates; working knowledge of machine learning in production; experience coaching leaders and building succession plans.

Required skills

• Strong Java, including multithreading, high-concurrency patterns, I/O/NIO and network programming.
• Distributed systems and highly available, high-throughput architectures (caching, partitioning, consistency models).
• Spring or Spring Boot, HTTP, TCP/IP, REST, Gradle or Maven.
• Docker and Kubernetes.
• Preferred: Cassandra, Redis, NoSQL, Hadoop, Kafka, Flink or Spark; Kotlin, Scala, Python or Golang; Alibaba Cloud, AWS or GCP; rule engines, feature stores, model serving, A/B testing and real-time stream processing; PCI DSS, SOC 2 and data privacy awareness.