Software Engineer Ii, Machine Learning Platform

**About Airwallex**

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 team**

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 team builds and maintains the robust, scalable infrastructure that powers our advanced machine learning models, enabling rapid iteration and deployment of risk strategies.

**What you’ll do**

Our mission is to keep Airwallex's products and services safe and secure, and make Airwallex a trusted partner for businesses around the world. You will be instrumental in designing, building, and optimizing the end-to-end machine learning platform that enables rapid development, deployment, and monitoring of ML models. We leverage cutting-edge technologies, including big data frameworks, real-time streaming, MLOps best practices, graph technologies, and Large Language Models (LLMs), to implement and improve our strategy.

Our team expands across Beijing, Shanghai and Singapore. We collaborate with other teams (Data Science, Product, Engineering) and our customers globally to ensure a holistic approach for risk management and deliver state-of-the-art ML capabilities.

**Responsibilities**:
- Design, build, and maintain scalable and reliable data pipelines for ingesting, processing, and transforming large datasets (batch and stream) for ML model training and inference.- Develop and manage feature generation systems and feature stores, ensuring data quality, consistency, reusability, and accessibility for model development.- Architect and implement robust model serving infrastructure for deploying, managing, and monitoring machine learning models in production at scale, ensuring low latency and high availability.- Collaborate with Data Scientists, ML Engineers, and Senior Technical Staff to understand ML requirements and translate them into robust platform capabilities and infrastructure.- Champion and implement MLOps best practices, including CI/CD for ML, model versioning, experiment tracking, automated monitoring, and feedback loops.- Experiment with new technologies and frameworks in the big data, streaming, and MLOps space, proposing architectural improvements for the ML platform.- Support and mentor less-experienced team members in ML platform engineering, big data technologies, data pipeline development, and MLOps practices.- Ensure the security, scalability, performance, and cost-effectiveness of the ML platform components.

**Who you are** We're looking for people who meet the minimum qualifications for this role. The preferred qualifications are great to have, but are not mandatory.

**Minimum qualifications**:
- More than 3 years of software engineering experience, with at least 1+ years focused on building and maintaining machine learning platforms, big data systems, or large-scale data infrastructure.- Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.- Proven experience with big data technologies (e.g., Apache Spark, Hadoop, Presto, Hive, Flink) and distributed computing.- Strong proficiency in at least one programming language commonly used in data engineering and ML (e.g., Python, Scala, Java).- Experience in designing, implementing, and managing complex ETL/ELT data pipelines and workflow management tools (e.g., Apache Airflow, Kubeflow Pipelines, Dagster).-
- Experience with designing and implementing solutions for feature generation, feature engineering at scale, and/or feature stores.- Familiarity with model serving patterns, tools, and infrastructure (e.g., KFServing, Seldon Core, BentoML, Triton Inference Server, or custom API development for model deployment).- Experience with containerization (e.g., Docker) and orchestration (e.g., Kubernetes).- Proficient with build tools (e.g., Gradle, Maven, SBT) and version control systems (e.g., Git).

**Preferred qualifications**:
- In-