Senior Software Engineer, Data and AI Infrastructure Engineering Engineering • Seattle Americas , Seattle Seattle

Airwallex; Seattle, Northern (WA, KY); Hybrid; USD 180,000 - 260,000; Full time

Job summary

Airwallex seeks a Senior Software Engineer to build the infrastructure powering our data and AI platforms. You will design and operate distributed systems supporting high-throughput data processing, real-time workloads, and production AI applications, evolving Kubernetes and cloud foundations, and building AI traffic management and model serving.

This high-impact role involves working across application, data, ML, security, and infra teams to establish the technical foundations for Airwallex’s

Qualifications

• 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 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 production code experience.
• Solid understanding of distributed-systems: availability, consistency, fault tolerance, backpressure, horizontal scalability.
• Experience with infrastructure-as-code, automated delivery, and modern observability practices.
• Strong debugging skills and methodical cross-layer troubleshooting.
• Clear communication and cross-disciplinary collaboration.
• Ownership mindset: identify problems, drive to resolution, improve the underlying system.

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 Kafka, Spark, and Flink.
• Build and evolve AI infrastructure, including AI gateways, model-routing layers, traffic management, and observability.
• Develop self-service capabilities for data, AI, and application teams to deploy workloads safely and independently.
• Partner with engineering teams to translate data and AI requirements into durable platform capabilities.

Skills

Kubernetes

Go

Python

Java

Distributed systems

Ownership mindset

Debugging

Tools

Kafka

Spark

Flink

AI gateways

Model routing

AWS

GCP

Azure

Job description

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 250,000 businesses worldwide – including Brex, 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 2,300 of the brightest and most innovative people in tech across 27 offices around the globe. Valued at US$11 billion and backed by world‑leading investors including T. Rowe Price, Visa, Mastercard, Robinhood Ventures, Sequoia, Salesforce Ventures, DST Global, and Lone Pine Capital, 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.

Attributes We Value

We hire successful builders with founder‑like energy who want real impact, accelerated learning, and true ownership. You bring strong role‑related expertise and sharp thinking, and you’re motivated by our mission and operating principles. You move fast with good judgment, dig deep with curiosity, and make decisions from first principles, balancing speed and rigor.

You’re humble and collaborative; turn zero‑to‑one ideas into real products, and you “get stuff done” end‑to‑end. You use AI to work smarter and solve problems faster. Here, you’ll tackle complex, high‑visibility problems with exceptional teammates and grow your career as we build the future of global banking. If that sounds like you, let’s build what’s next.

About the role and 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 is a hybrid role based in Seattle, WA.

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.

What We’re Looking For

• 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.

Bonus Points

• 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.