As an Engineering Productivity Engineer, you will own the tools, infrastructure, and automation that Aria’s engineering teams use to build, test, and ship software — from CI/CD pipelines and test frameworks to the hardware and virtual switch testbeds they run on. You will also build and operate the AI layer of our engineering workflow, from high-throughput inference clusters to agentic copilots for failure triage, test generation, and code review. You will solve hard problems in distributed systems, virtualization, and platform automation across Linux/UNIX environments that support software development and switch testing. This role spans Aria’s end-to-end engineering stack, from developer tooling through release gating.

Responsibilities

• Design, build, and maintain high-performance CI/CD pipelines and developer tooling that give engineers fast feedback and reliable code integration
• Architect and scale test execution frameworks and automated failure analysis for hardware and virtual switch testbeds
• Build, troubleshoot, and optimize virtualized and containerized environments on Docker, Kubernetes, and virtualization platforms, managed as infrastructure as code and designed for high availability
• Develop monitoring, logging, and analytics on time-series and relational databases to catch system bottlenecks early and triage build and test failures
• Deploy and operate AI inference clusters that serve engineering workflows at high throughput
• Build GenAI workflows and agentic copilots for log and crash-dump triage, test generation, and automated code review
• Partner with switch software, server infrastructure, and AI/ML teams to set and uphold standards for code quality, regression testing, and release gating

Qualifications

• BS/MS in Computer Science, Software Engineering, or equivalent, with 3+ years of experience in systems engineering, DevOps, or developer infrastructure on Linux/UNIX platforms
• Strong engineering fundamentals in Python, Go, C++, or Rust for building low-latency, scalable backend infrastructure
• Hands-on expertise with Docker, Kubernetes, virtual machine environments, infrastructure-as-code tooling, and cloud automation
• Proficiency in designing and maintaining test automation frameworks (PyTest or custom runners) and CI/CD systems such as GitHub Actions
• Experience with time-series or columnar analytics databases such as ClickHouse, Prometheus, or InfluxDB for log and metric aggregation
• Experience building LLM- or RAG-based developer copilots, operating AI inference clusters, or designing automated AI triage systems is a plus
• Familiarity with network telemetry, white-box switches, or SONiC is a plus