Level

Airwallex

Senior Software Engineer, Infrastructure (Data & AI)

AI in this role

Senior Infrastructure Engineer building scalable platforms for data processing, real-time workloads, and AI model serving.

vllmkuberneteskafkasparkflink
distributed-systemsinfrastructuredata-engineeringmlops

About Airwallex

Airwallex is the AI-native financial operating system for a real-time, intelligent economy. More than 676,000 businesses, including McLaren Racing, Qantas, SHEIN, and TikTok, use us, directly or through our platform partners, to run their financial operations or build and monetize financial products of their own.

We started in Melbourne in 2015 to build the infrastructure global commerce runs on. We're the regulated backbone behind global payments: not by accident, but by design. A decade plus, 85+ licenses, and a financial infrastructure spanning North America, Europe, the Middle East, and Asia-Pacific.

We're co-headquartered in San Francisco and Singapore, with more than 2,300 people across 27 offices. We hire builders with founder-level energy, people who move fast with good judgment, dig in with real curiosity, and make calls from first principles rather than waiting to be told what to do. Read our operating principles to see it in full.

 

About The Team

We are looking for a Senior Software Engineer to build the infrastructure that powers our data and AI platforms.

You will design and operate distributed systems that support high-throughput data processing, real-time workloads, and production AI applications. This includes evolving our Kubernetes and cloud foundations, improving the reliability and scalability of platforms such as Kafka, Spark, and Flink, and building infrastructure for AI traffic management and model serving.

This is a high-impact role for an engineer who enjoys solving complex infrastructure problems, writing production software, and giving other engineering teams reliable self-service platforms. You will work across application, data, machine learning, security, and infrastructure teams to establish the technical foundations for the company’s next stage of growth.

This role is based in Seoul, South Korea.

Address: Units 1006, Partners Tower, 83 Gasan digital 1-ro, Geumcheon-gu, Seoul, Republic of Korea, 08589 서울 금천구 가산디지털1로 83, 1006 호(가산동, 파트너스타워)

What You’ll Do

  • 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.

  • Build and evolve AI infrastructure, including AI gateways, model-routing layers, traffic management, rate limiting, authentication, observability, and usage controls.

  • Develop self-service capabilities that enable data, AI, and application teams to deploy and operate workloads safely and independently.

  • Partner with engineering teams to translate emerging data and AI requirements into durable platform capabilities.

Who You Are

  • 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 technologies such as 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, such as AWS, Google Cloud, or Microsoft Azure.

  • Strong knowledge of cloud and container networking, including DNS, load balancing, ingress, service discovery, TLS, routing, and network security.

  • Proficiency in one or more of Go, Python, or Java, with experience writing maintainable production software.

  • A solid understanding of distributed-systems concepts, including availability, consistency, fault tolerance, backpressure, and horizontal scalability.

  • Experience operating critical infrastructure using infrastructure-as-code, automated delivery, and modern observability practices.

  • Strong debugging skills and the ability to work methodically across multiple layers of a complex system.

  • Clear communication skills and a track record of collaborating effectively across engineering disciplines.

  • An ownership mindset: you identify important problems, drive them to resolution, and improve the underlying system rather than treating symptoms.

Especially Valuable Experience

  • Platform engineering experience, particularly building internal developer platforms or paved-road workflows used by multiple engineering teams.

  • Hands-on experience with serving technologies such as SGLang, vLLM, or NVIDIA Triton Inference Server.

  • Knowledge of GPU scheduling, batching, model parallelism, memory management, autoscaling, and inference-performance optimization.

  • Experience improving the cost efficiency of large-scale data processing or AI inference workloads.

  • Contributions to infrastructure, data-platform, Kubernetes, or AI-serving open-source projects.

What Success Looks Like

  • Delivered meaningful improvements to the scalability, reliability, or efficiency of our data and AI infrastructure.

  • Reduced the operational effort required to deploy and manage data or AI workloads.

  • Improved visibility into system performance, reliability, capacity, and cost.

  • Established reusable platform capabilities adopted by engineering teams.

  • Helped define the technical direction for our next generation of data and AI infrastructure.

Applicant Safety Policy: Fraud and Third-Party Recruiters

To protect you from recruitment scams, please be aware that Airwallex will not ask for bank details, sensitive ID numbers (i.e. passport), or any form of payment during the application or interview process. All official communication will come from an @airwallex.com email address. Please apply only through careers.airwallex.com or our official LinkedIn page.

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.

Equal opportunity

Airwallex 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.

How we rate this

Senior Software Engineer, Infrastructure (Data & AI) at Airwallex rates 80 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Distributed SystemsInfrastructureData EngineeringMlopsvLLMKubernetesKafkaSpark

Questions you could be asked

  1. Tell me about a project where distributed systems was part of your work. What did you do?
  2. Tell me about a project where infrastructure was part of your work. What did you do?
  3. Tell me about a project where data engineering was part of your work. What did you do?
  4. Tell me about a project where mlops was part of your work. What did you do?
  5. Walk me through how you've used vLLM in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: Distributed Systems, Infrastructure, Data Engineering, Mlops, and vLLM. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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