Description
Compensation
The expected base pay range for this role is $150K – $245K, with potential bonus.
How you'll make impact
You'll design, build, and operate highly available data and AI infrastructure on Kubernetes and public cloud platforms. Your responsibilities will include:
- Improving the scalability, reliability, efficiency, and cost visibility of production data and AI workloads.
- Building self-service capabilities for data, AI, and application teams to deploy and operate workloads safely and independently.
- Partnering across engineering to turn emerging data and AI requirements into durable platform capabilities.
- Delivering the technical foundations for the next generation of data and AI infrastructure.
- Driving platform capabilities that help engineering teams operate critical workloads with greater independence.
What we're looking for
Essentials
- 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.
- 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.
- Proficiency in one or more of Go, Python, or Java, with experience writing maintainable production software.
- A solid understanding of distributed-systems concepts.
- 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.
Preferred
- Platform engineering experience, particularly building internal developer platforms.
- 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.
Learn more about your team
The Infrastructure & Productivity team at Airwallex builds the technical foundation that allows the global engineering organization to scale.