Senior Software Engineer, Infrastructure (Data & AI)

About the role

Airwallex, the AI-native financial operating system for global commerce, is hiring a Senior Software Engineer, Infrastructure (Data & AI) in Seoul. You will build and operate the infrastructure powering its data and AI platforms, designing highly available systems on Kubernetes and public cloud platforms and developing scalable foundations for streaming, batch processing and real-time workloads.

Key responsibilities

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

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.

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, internal developer platforms, serving technologies (SGLang, vLLM, NVIDIA Triton), GPU scheduling and inference optimization