Senior Software Engineer, Data and AI Infrastructure

Airwallex

. Design, build, and operate highly available data and AI infrastructure on Kubernetes and public cloud platforms .

Posted 9/17/2026full-timeSeattle • Washington • United StatesSenior💰 $150,000 - $245,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and operating scalable data and AI infrastructure on Kubernetes and public cloud platforms, with a strong focus on improving system performance, reliability, and cost efficiency. Proficient in collaborating across engineering disciplines to translate requirements into durable platform capabilities.

Highest-signal resume keywords

Kubernetes Environment DesignDistributed Data Infrastructure (Kafka, Spark, Flink)Cloud Platforms (AWS, Google Cloud, Microsoft Azure)Programming (Go, Python, Java)Infrastructure-as-Code Practices

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

KubernetesKafkaSparkFlinkAWSGoogle CloudMicrosoft AzureGoPythonJava

Soft Skills

Clear CommunicationOwnership MindsetCollaboration

Tools & Technologies

AI GatewaysModel-Routing PlatformsInfrastructure-as-CodeObservability ToolsSGLangVLLMNVIDIA Triton Inference Server

Industry Keywords

Data InfrastructureAI InfrastructureDistributed SystemsCloud NetworkingCost Efficiency

Tech Stack

Tools & technologies

AWSAzureCloudDNSJavaKafkaKubernetesPythonSparkGo

About the role

Key responsibilities & impact

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, rate limiting, authentication, observability, and usage controls
Develop self-service capabilities enabling data, AI, and application teams to deploy and operate workloads safely and independently
Partner with application, data, machine learning, security, and infrastructure teams to translate emerging requirements into durable platform capabilities
Improve the scalability, reliability, and efficiency of data and AI infrastructure
Reduce operational effort for deploying and managing data or AI workloads
Improve visibility into system performance, reliability, capacity, and cost
Establish reusable platform capabilities adopted by engineering teams
Help define the technical direction for next-generation data and AI infrastructure

Requirements

What you’ll need

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 developing AI infrastructure such as AI gateways or model-routing platforms
Hands-on experience with 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 Go, Python, or Java, with experience writing maintainable production software
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 ability to work methodically across multiple layers of a complex system
Clear communication skills and track record of collaborating across engineering disciplines
Ownership mindset and ability to drive important problems to resolution
Platform engineering experience, particularly building internal developer platforms or paved-road workflows, is a bonus
Hands-on experience with SGLang, vLLM, or NVIDIA Triton Inference Server is a bonus
Knowledge of GPU scheduling, batching, model parallelism, memory management, autoscaling, and inference-performance optimization is a bonus
Experience improving cost efficiency of large-scale data processing or AI inference workloads is a bonus
Contributions to infrastructure, data-platform, Kubernetes, or AI-serving open-source projects are a bonus

Benefits

Comp & perks

Annual bonuses may be offered
RSUs or other additional compensation may be offered
Medical, dental, and vision insurance for US-based employees
401(k) plan
Short-term and long-term disability coverage
Basic life insurance
Well-being benefits
20 paid days of vacation per calendar year
12 paid days of company holidays per calendar year
Statutory minimum paid annual leave and public holidays