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The Risk Platform team at Airwallex is responsible for managing the risk for all the products at Airwallex, including GTPN, PA, Issuing, Onboarding, and Account takeover. The risk landscape is constantly changing, and fraudsters are becoming increasingly sophisticated. We are at the forefront of innovation in risk management. Our mission is to keep Airwallex's products and services safe and secure, and make Airwallex a trusted partner for businesses around the world. We use cutting-edge technologies, such as graph, ML, and LLM, to implement and improve our strategy. We collaborate with other teams and our customers globally to ensure a holistic approach for risk management.
Job Responsibility:
Lead the technical direction of core risk services, including real-time detection, decision engines, and risk tooling, ensuring they are reliable, scalable, and cost-efficient under high throughput and low-latency constraints
Design and evolve system architecture for distributed, event-driven risk systems (microservices, streaming pipelines, feature stores, model serving layers) that support global products and regulatory requirements
Own end-to-end delivery of complex initiatives: from high-level design and technical specifications, to implementation, rollout strategies, and continuous improvement
Be deeply hands-on in code and design, writing high-quality production code (primarily in Java/Spring Boot) and driving high-impact design reviews, RFCs, and architecture discussions
Partner closely with the Engineering Manager and Product Manager to shape the roadmap, define technical milestones, and translate business and risk objectives into robust engineering solutions
Champion engineering excellence by defining and enforcing standards for code quality, observability, reliability, security, and performance across the Risk Platform stack
Improve detection effectiveness by working with data and ML teams to integrate models, rules, and features into production systems, and by designing experimentation and evaluation capabilities
Mentor and uplevel other engineers, including senior ICs: provide technical coaching, pair programming, design guidance, and feedback that helps them do the best work of their careers
Collaborate across teams (platform, data, SRE, security, and other product engineering teams) to define clear interfaces, SLAs, and ownership boundaries for shared services and infrastructure
Identify and pay down technical debt deliberately, balancing short-term impact with long-term platform health and enabling faster, safer delivery over time
Requirements:
10+ years of back-end engineering experience, including substantial time owning and operating complex, distributed systems in production
Deep experience in risk, fraud, or fintech systems (e.g., payments, banking, trading, or similar high-stakes domains where correctness and reliability are critical)
Strong proficiency in Java, including multi-threading, high-concurrency patterns, performance tuning, and networked service design (I/O/NIO, HTTP/TCP, REST)
Hands-on experience with distributed system design, including event-driven architectures, partitioning/sharding, consistency models, caching strategies, and resiliency patterns
Proficiency with Spring / Spring Boot and build tools such as Gradle or Maven
Practical experience with containerization and orchestration, particularly Docker and Kubernetes, in production environments
Solid understanding of observability and operations: logging, metrics, tracing, dashboards, and incident management for large-scale systems
Ability to lead complex technical initiatives end-to-end, influencing engineers and stakeholders without relying on formal management authority
Strong communication skills, with the ability to explain complex technical topics clearly to engineers, product managers, and non-technical stakeholders
Bachelor’s degree in Computer Science or a related field, or equivalent practical experience
Nice to have:
Experience with risk-specific components, such as rule engines, feature stores, model serving frameworks, real-time stream processing, and A/B testing / experimentation platforms
Working knowledge of machine learning in production, including model lifecycle, feature engineering, evaluation metrics, and online/offline consistency
Experience with modern data and storage technologies, such as Cassandra, Redis, NoSQL databases, Hadoop, and streaming systems like Kafka, Flink, or Spark
Polyglot engineering experience with one or more of Kotlin, Scala, Python, or Golang
Cloud experience with Alibaba Cloud, AWS, or GCP, including cloud networking, security best practices, and cost awareness
Familiarity with regulatory and security standards relevant to payments and financial services (e.g., PCI DSS, SOC 2, data privacy and protection best practices)
Demonstrated impact as a Staff or Principal Engineer (or equivalent) in high-growth, high-scale environments