# Senior Software Engineer, Data and AI Infrastructure **Company:** [Airwallex](https://scaleengineer.com/companies/airwallex) Senior Software Engineer focused on designing and operating high-throughput distributed data and AI infrastructure systems on Kubernetes and public cloud platforms at Airwallex. This role partners with data, ML, security, and infrastructure teams to build scalable platforms for streaming, batch processing, and AI model serving while enabling self-service capabilities for engineering teams. Requires 5+ years of DevOps, SRE, or platform engineering experience with strong production Kubernetes expertise, distributed data systems knowledge, and proficiency in Go, Python, or Java. **Role:** Backend Engineer **Seniority:** Senior **Locations:** US - Seattle **Salary:** 150000–245000 USD [Apply](https://jobs.ashbyhq.com/airwallex/f5be8477-ca01-418b-bd22-946430d28831) Canonical: https://scaleengineer.com/jobs/airwallex/senior-software-engineer-data-and-ai-infrastructure --- ## Responsibilities - Design and Operate Distributed Infrastructure: Design, build, and operate highly available data and AI infrastructure on Kubernetes and public cloud platforms, ensuring reliability and performance at scale. Lead architectural decisions for complex distributed systems handling high-throughput data processing and real-time workloads. - Develop Scalable Data Processing Platforms: Develop and evolve scalable platforms for streaming, batch processing, and real-time data workloads using technologies such as Kafka, Spark, and Flink. Optimize performance, throughput, and resource utilization across data pipeline infrastructure. - Build AI Infrastructure and Model Serving: Build and evolve AI infrastructure components including AI gateways, model-routing layers, traffic management, rate limiting, authentication, observability, and usage controls. Establish technical foundations for production AI applications and model deployment. - Enable Self-Service Platform Capabilities: Develop self-service capabilities and internal developer platforms that enable data, AI, and application teams to deploy and operate workloads safely and independently. Create paved-road workflows that reduce operational friction and improve developer productivity. - Collaborate Across Engineering Disciplines: Partner with application, data, machine learning, security, and infrastructure teams to translate emerging business and technical requirements into durable, reusable platform capabilities. Drive technical direction and establish best practices for the next generation of infrastructure. - Improve System Reliability and Cost Efficiency: Drive meaningful improvements to scalability, reliability, and efficiency of data and AI infrastructure. Reduce operational effort required to deploy and manage workloads while optimizing cost efficiency of large-scale data processing and AI inference workloads. ## Requirements ### education - {"name":"Bachelor's Degree in Computer Science or Related Field","description":"Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or equivalent practical experience. Strong foundation in computer science fundamentals, algorithms, and systems design."} ### technical - {"name":"Production Kubernetes Expertise","description":"Strong hands-on experience designing, operating, and troubleshooting production Kubernetes environments at scale, including cluster management, workload orchestration, networking, storage, and observability."} - {"name":"Distributed Data Infrastructure Systems","description":"Proven experience building or operating distributed data infrastructure with technologies such as Apache Kafka, Apache Spark, or Apache Flink. Deep understanding of stream processing, batch processing, and event-driven architectures."} - {"name":"AI Infrastructure and Model Serving","description":"Experience developing or operating AI infrastructure such as AI gateways, model-routing platforms, or inference serving systems. Familiarity with model serving technologies, rate limiting, traffic management, and production ML deployment patterns."} - {"name":"Public Cloud Platform Mastery","description":"Hands-on production experience with at least one major public cloud platform such as AWS, Google Cloud Platform, or Microsoft Azure. Strong knowledge of cloud services, networking, security, and infrastructure-as-code."} - {"name":"Cloud and Container Networking","description":"Strong knowledge of cloud and container networking fundamentals including DNS, load balancing, ingress controllers, service discovery, TLS/SSL, routing protocols, and network security best practices."} - {"name":"Programming Language Proficiency","description":"Proficiency in one or more of Go, Python, or Java with demonstrated ability to write maintainable, well-tested production software. Experience with systems programming, performance optimization, and distributed systems development."} - {"name":"Distributed Systems Fundamentals","description":"Solid understanding of core distributed-systems concepts including availability, consistency models, fault tolerance, backpressure handling, horizontal scalability, and CAP theorem implications."} - {"name":"Infrastructure-as-Code and Automation","description":"Experience operating critical infrastructure using infrastructure-as-code tools, automated delivery pipelines, continuous integration and deployment, and modern observability and monitoring practices."} - {"name":"Advanced Debugging and Troubleshooting","description":"Strong debugging skills and methodical approach to troubleshooting complex systems across multiple layers including application, platform, and infrastructure levels. Proficiency with diagnostic tools and observability solutions."} ### experience - {"name":"DevOps, SRE, or Platform Engineering","description":"5+ years of hands-on experience in DevOps, Site Reliability Engineering (SRE), or platform engineering roles with demonstrated track record of owning production systems end-to-end and driving reliability improvements."} - {"name":"Production Systems Ownership","description":"Proven experience taking full ownership of production systems including design, implementation, deployment, monitoring, incident response, and capacity planning. Track record of building and maintaining systems that serve millions of requests or process massive data volumes."} - {"name":"Cross-Functional Collaboration","description":"Track record of collaborating effectively across multiple engineering disciplines including application engineering, data engineering, machine learning, and security teams. Experience translating technical requirements into platform solutions."} - {"name":"System Optimization and Performance","description":"Experience optimizing system performance, reliability, and cost efficiency at scale. Track record of identifying bottlenecks, reducing operational overhead, and improving resource utilization in complex infrastructure environments."} ## Skills ### required - {"name":"Kubernetes","description":"Production-grade experience with Kubernetes cluster operations, networking, storage, security, and observability. Experience with multiple Kubernetes distributions and cloud-native deployment patterns."} - {"name":"Apache Kafka","description":"Hands-on experience deploying, operating, and troubleshooting Apache Kafka clusters in production. Understanding of partitioning, replication, consumer groups, and performance tuning."} - {"name":"Apache Spark","description":"Production experience with Apache Spark for batch and streaming data processing. Understanding of RDD/DataFrame APIs, distributed execution, resource management, and performance optimization."} - {"name":"Go, Python, or Java","description":"Expert-level proficiency in at least one of Go, Python, or Java for systems programming and infrastructure tooling. Ability to write production-quality code with strong testing and documentation practices."} - {"name":"AWS, GCP, or Azure","description":"Deep hands-on experience with at least one major cloud provider's infrastructure services, networking, managed services, IAM, and cost optimization strategies."} - {"name":"Container Networking and DNS","description":"Expert understanding of container networking models, DNS resolution, service discovery mechanisms, load balancing strategies, and network security in containerized environments."} - {"name":"Infrastructure-as-Code","description":"Proficiency with infrastructure-as-code tools such as Terraform, CloudFormation, Helm, or Kustomize. Experience managing large-scale infrastructure configurations and deployment automation."} - {"name":"Distributed Systems Design","description":"Strong grasp of distributed systems principles including consensus algorithms, replication strategies, fault tolerance, and trade-offs between consistency, availability, and partition tolerance."} - {"name":"Observability and Monitoring","description":"Experience implementing comprehensive observability solutions including metrics, logging, tracing, and alerting. Familiarity with tools like Prometheus, Grafana, ELK stack, or Datadog for infrastructure visibility."} - {"name":"System Troubleshooting and Debugging","description":"Proficiency in debugging distributed systems using tools like strace, tcpdump, flamegraphs, and profilers. Ability to systematically isolate and resolve issues across the entire stack."} ### preferred - {"name":"Platform Engineering and Internal Developer Platforms","description":"Experience building and operating internal developer platforms or paved-road workflows used by multiple engineering teams. Understanding of developer experience optimization and self-service infrastructure."} - {"name":"AI Model Serving Technologies","description":"Hands-on experience with AI model serving frameworks such as SGLang, vLLM, NVIDIA Triton Inference Server, or similar technologies. Understanding of inference performance optimization and model deployment patterns."} - {"name":"GPU Computing and ML Infrastructure","description":"Knowledge of GPU scheduling, resource management, batching, model parallelism, memory optimization, autoscaling strategies, and inference-performance optimization techniques for large language models and other ML workloads."} - {"name":"Cost Optimization","description":"Demonstrated experience improving cost efficiency of large-scale data processing or AI inference workloads through resource optimization, scaling strategies, and architectural improvements."} - {"name":"Open Source Infrastructure Contributions","description":"Contributions to infrastructure, data-platform, Kubernetes, or AI-serving open-source projects. Active involvement in the infrastructure and platform engineering community."} - {"name":"Apache Flink","description":"Production experience with Apache Flink for stream processing, understanding of stateful computations, event time processing, and exactly-once semantics."} - {"name":"Network Protocol Knowledge","description":"Deep understanding of network protocols including TCP/IP, HTTP/2, gRPC, and related technologies. Experience optimizing network performance and debugging connectivity issues."} - {"name":"Machine Learning Operations","description":"Experience with MLOps platforms, model deployment pipelines, feature stores, and tools for managing machine learning workloads in production environments."} ## Tech stack ### tools - {"name":"Terraform","description":"Infrastructure-as-code tool for provisioning and managing cloud resources, Kubernetes clusters, and infrastructure automation across AWS, GCP, or Azure."} - {"name":"Helm","description":"Kubernetes package manager for templating, distributing, and deploying applications and infrastructure components in Kubernetes environments."} - {"name":"Prometheus","description":"Time-series monitoring and alerting system for collecting metrics from infrastructure and applications, providing visibility into system performance and health."} - {"name":"Grafana","description":"Visualization and dashboarding platform for monitoring infrastructure metrics, logs, and traces collected from Prometheus and other observability sources."} - {"name":"Docker","description":"Container runtime and platform for building, packaging, and distributing containerized applications and infrastructure components."} - {"name":"Git","description":"Version control system for managing infrastructure-as-code, tooling, and collaborative development across the infrastructure team."} ### others - {"name":"AWS, Google Cloud Platform, or Microsoft Azure","description":"Major public cloud platforms providing compute, networking, storage, and managed services for hosting data and AI infrastructure."} - {"name":"gRPC","description":"High-performance RPC framework for building efficient communication between distributed services and infrastructure components."} - {"name":"TLS/SSL Encryption","description":"Transport layer security protocols for securing communication between infrastructure components and external services."} - {"name":"DNS and Service Discovery","description":"Domain Name System and service discovery mechanisms for routing traffic and enabling communication in distributed Kubernetes environments."} - {"name":"Load Balancing and Ingress","description":"Load balancing strategies and ingress controllers for managing traffic distribution, routing, and service exposure in Kubernetes clusters."} ### databases - {"name":"Distributed Data Stores","description":"Experience with distributed databases and data stores optimized for analytics, time-series data, and large-scale data warehousing in cloud environments."} ### languages - {"name":"Go","description":"Systems programming language used for building high-performance infrastructure tooling, cloud-native applications, and microservices within the data and AI platform."} - {"name":"Python","description":"Used for data processing, automation scripts, infrastructure orchestration, and building tools that interface with data and ML platforms."} - {"name":"Java","description":"Core language for distributed data processing frameworks like Apache Spark and other JVM-based infrastructure components."} ### frameworks - {"name":"Kubernetes","description":"Container orchestration platform for deploying, scaling, and managing containerized data and AI workloads across distributed infrastructure."} - {"name":"Apache Kafka","description":"Distributed event streaming platform for high-throughput, real-time data processing and message brokering across the platform infrastructure."} - {"name":"Apache Spark","description":"Unified analytics engine for large-scale data processing supporting both batch and streaming workloads across distributed clusters."} - {"name":"Apache Flink","description":"Stream processing framework for distributed event-driven computations with strong support for stateful operations and event-time semantics."} - {"name":"vLLM","description":"High-throughput LLM inference engine used for efficient model serving and generation optimization in AI infrastructure."} - {"name":"SGLang","description":"Structured generation language for efficient large language model inference and serving with structured outputs."} - {"name":"NVIDIA Triton Inference Server","description":"Multi-framework, multi-GPU inference serving platform for deploying and managing production machine learning models at scale."} ## Benefits ### benefits ## Compensation - **max:** 240000 - **min:** 180000 - **currency:** USD - **stockOptions:** true ## Interview process ### steps ## Full 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](https://www.airwallex.com/us/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_](http://airwallex.com) _email address. Please apply only through_ [_careers.airwallex.com_](http://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.