Airwallex is the only unified payments and financial platform for global businesses. Powered by our unique combination of proprietary infrastructure and software, we empower over 150,000 businesses worldwide – including Brex, Rippling, Navan, Qantas, SHEIN and many more – with fully integrated solutions to manage everything from business accounts, payments, spend management and treasury, to embedded finance at a global scale.
Proudly founded in Melbourne, we have a team of over 1,700 of the brightest and most innovative people in tech across 26 offices around the globe. Valued at US$6.2 billion and backed by world-leading investors including Visa, Airtree, Blackbird, Sequoia, DST Global, Greenoaks, Salesforce Ventures, Lone Pine, and Square Peg, Airwallex is leading the charge in building the global payments and financial platform of the future. If you’re ready to do the most ambitious work of your career, join us.
About the teamThe Client Risk Team at Airwallex is responsible for proactively detecting and managing client-level financial crime and credit risk across all Airwallex products. Our vision is to deliver a unified and data-driven view of each client by consolidating internal and external insights. We are committed to leveraging advanced machine learning and large language models (LLMs) to enhance decision intelligence, automate operational workflows, and support responsible business growth globally.
What you’ll doAs a Senior Software Engineer in Client Risk, you will design and build scalable risk monitoring systems using AI, ML, and LLM technologies. You will help automate review processes, consolidate risk insights, and develop unified client management tools. You will also define and implement evaluation frameworks to measure the effectiveness of our AI-powered solutions, collaborating with product, engineering, operations, and data teams to drive innovation in risk management.
This role is based in "Singapore"
Responsibilities:Design, build, and maintain cutting-edge risk monitoring systems that proactively detect and mitigate client-level risks using AI (ML, LLMs, agent frameworks) and advanced data analytics.
Develop automated name screening, sanction checking, and anti-money laundering solutions powered by LLM agents, aligned with operational team workflows.
Build seamless client management systems that unify internal and external data into a single source of truth for risk intelligence and decisioning.
Establish robust agent evaluation methodologies and pipelines to objectively measure and demonstrate the effectiveness of AI-driven automation solutions.
Construct and support scalable, highly available, and low-latency distributed systems on public cloud infrastructure (e.g., GCP/AWS), ensuring quality, reliability, and strong data governance.
Build and maintain reliable data pipelines, data quality monitoring, and real-time streaming services.
Collaborate with global, cross-functional teams—including product, engineering, operations, compliance, and data science—to drive impactful risk solutions and operational excellence.
Bachelor’s degree in Computer Science, Software Engineering, or a related technical discipline, or equivalent practical experience.
Solid backend engineering skills; experience with Java, Python, or Kotlin (Spring / Spring Boot preferred).
Experience designing and developing scalable, high-concurrency, high-availability distributed systems, ideally in a cloud environment.
Strong ability to translate complex business requirements into robust technical solutions, with a focus on risk intelligence and automation.
Proficiency with modern server technologies, data modeling, and performance optimization.
Clear orientation towards measurable business and customer outcomes.
Excellent communication and collaboration skills with cross-functional partners; proactive, pragmatic, and driven.
Experience in or knowledge of financial crime, risk management, compliance, or fintech domains.
Proven experience with LLM agent frameworks, such as building Retrieval-Augmented Generation (RAG) pipelines, langchain, langfuse, or MCP.
Experience implementing production-grade AI/LLM-driven workflow automation to streamline operational review processes.
Hands-on machine learning experience for predictive risk analytics, fraud detection, or operational automation.
Familiarity with real-time data streaming (e.g., Apache Kafka/Flink) and big data technologies (e.g., Spark, BigQuery, Airflow, ElasticSearch).
Experience designing agent and automation evaluation frameworks to ensure solution effectiveness and continuous improvement.
Previous experience in a high-growth, fast-paced fintech, payments, or technology-driven organization is a plus.
Join us to shape the future of client risk management by transforming how Airwallex detects, investigates, and mitigates risk with the latest in AI and automation!
Equal opportunityAirwallex is proud to be an equal opportunity employer. We value diversity and anyone seeking employment at Airwallex is considered based on merit, qualifications, competence and talent. We don’t regard color, religion, race, national origin, sexual orientation, ancestry, citizenship, sex, marital or family status, disability, gender, or any other legally protected status when making our hiring decisions. If you have a disability or special need that requires accommodation, please let us know.
Airwallex does not accept unsolicited resumes from search firms/recruiters. Airwallex will not pay any fees to search firms/recruiters if a candidate is submitted by a search firm/recruiter unless an agreement has been entered into with respect to specific open position(s). Search firms/recruiters submitting resumes to Airwallex on an unsolicited basis shall be deemed to accept this condition, regardless of any other provision to the contrary.
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