Senior Software Engineer, Data and AI Infrastructure

About the role

Airwallex, the AI-native financial operating system for global commerce, is hiring a Senior Software Engineer for its Data and AI Infrastructure team in Seattle. The team builds the technical foundation that lets the global engineering organization scale, managing core platforms such as Kubernetes, Kafka, CI/CD pipelines and observability systems. The engineer works from the Seattle office three days a week.

Key responsibilities

• Design, build and operate highly available data and AI infrastructure on Kubernetes and public cloud platforms.
• Develop scalable platforms for streaming, batch processing and real-time data workloads using technologies such as Kafka, Spark and Flink, and build AI infrastructure including gateways, model-routing layers, traffic management, rate limiting, authentication, observability and usage controls.
• Improve the scalability, reliability, efficiency and cost visibility of production data and AI workloads.
• Build self-service capabilities so data, AI and application teams can deploy and operate workloads safely and independently.
• Partner with engineering to turn emerging data and AI requirements into durable platform capabilities.

Required profile

• 5+ years in DevOps, SRE or platform engineering, owning production systems end to end.
• Strong experience designing, operating and troubleshooting production Kubernetes environments.
• Experience with distributed data infrastructure (Kafka, Spark, Flink) or AI infrastructure (AI gateways, model-routing platforms).
• Hands-on experience with at least one major public cloud platform (AWS, Google Cloud or Microsoft Azure).
• Strong debugging skills, clear communication and an ownership mindset.
• Comfortable working across time zones with a globally distributed team.

Required skills

• Cloud and container networking (DNS, load balancing, ingress, service discovery, TLS, routing, network security)
• Go, Python or Java, with production software experience
• Distributed systems concepts (availability, consistency, fault tolerance, backpressure, horizontal scalability)
• Infrastructure-as-code, automated delivery and modern observability
• Preferred: platform engineering experience, serving technologies such as SGLang, vLLM or NVIDIA Triton, GPU scheduling and inference optimization, open-source contributions