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Airwallex is a unified payments and financial platform serving over 250,000 businesses globally, including Brex, Navan, Qantas, and SHEIN. The company provides integrated solutions for business accounts, payments, spend management, treasury, and embedded finance at scale. Founded in Melbourne with 2,300+ employees across 27 offices, Airwallex is valued at $11 billion and backed by leading investors including T. Rowe Price, Visa, Mastercard, Sequoia, and Salesforce Ventures.
You will design and operate distributed systems powering the company's data and AI platforms. This role focuses on building infrastructure for high-throughput data processing, real-time workloads, and production AI applications. Key responsibilities include evolving Kubernetes and cloud foundations, improving reliability and scalability of platforms like Kafka, Spark, and Flink, and building infrastructure for AI traffic management and model serving.
You will work across application, data, machine learning, security, and infrastructure teams to establish technical foundations for the company's next growth stage. Specific deliverables include designing highly available data and AI infrastructure on Kubernetes and public cloud platforms; developing scalable platforms for streaming, batch processing, and real-time data workloads; building AI infrastructure including gateways, model-routing layers, traffic management, rate limiting, authentication, observability, and usage controls; developing self-service capabilities for data, AI, and application teams; and partnering with engineering teams to translate emerging requirements into durable platform capabilities.
This is a hybrid role based in Seattle, WA. Success is measured by delivering meaningful improvements to scalability, reliability, or efficiency of data and AI infrastructure; reducing operational effort for deploying and managing workloads; improving visibility into system performance, reliability, capacity, and cost; establishing reusable platform capabilities adopted by engineering teams; and helping define technical direction for next-generation data and AI infrastructure.
REQUIREMENTS:
- 5+ years in DevOps, SRE, or platform engineering, owning production systems end to end
- Strong experience designing, operating, and troubleshooting production Kubernetes environments
- Experience building or operating distributed data infrastructure with Kafka, Spark, or Flink OR experience developing AI infrastructure such as AI gateways or model-routing platforms
- Hands-on experience with at least one major public cloud platform (AWS, Google Cloud, or Microsoft Azure)
- Strong knowledge of cloud and container networking (DNS, load balancing, ingress, service discovery, TLS, routing, network security)
- Proficiency in Go, Python, or Java with experience writing maintainable production software
- Solid understanding of distributed-systems concepts (availability, consistency, fault tolerance, backpressure, horizontal scalability)
- Experience operating critical infrastructure using infrastructure-as-code, automated delivery, and modern observability practices
- Strong debugging skills and ability to work methodically across multiple system layers
- Clear communication skills and track record of collaborating across engineering disciplines
- Ownership mindset: identifying important problems, driving them to resolution, and improving underlying systems
BONUS: Platform engineering experience building internal developer platforms; hands-on experience with SGLang, vLLM, or NVIDIA Triton Inference Server; knowledge of GPU scheduling, batching, model parallelism, memory management, autoscaling, and inference-performance optimization; experience improving cost efficiency of large-scale data processing or AI inference workloads; contributions to infrastructure, data-platform, Kubernetes, or AI-serving open-source projects.
