Hosted.ai is a turnkey, multi-tenant AI cloud platform enabling service providers to launch and scale GPU-as-a-Service (GPUaaS) with maximum efficiency. We are pioneering GPU over-commit and infrastructure optimization technologies to help enterprises and service providers run AI workloads more profitably.
Role Overview
hosted.ai is looking for an Automated QA Test Engineer who goes beyond scripting — someone with a strong investigative mindset. This role requires building robust automation frameworks while also diving deep into system behavior, debugging issues across Linux environments, containers, and distributed infrastructure.
You'll work on a complex, infrastructure-heavy platform, combining automation expertise (Python + Playwright) with hands-on debugging, exploratory testing, and root cause analysis.
Key Responsibilities
- Design and build scalable test automation frameworks using Python (pytest) and Playwright.
- Develop and execute end-to-end, integration, and regression test suites across UI and APIs.
- Define and drive test coverage strategies for critical platform components.
- Perform exploratory testing to uncover edge cases and failure scenarios beyond scripted tests.
- Investigate issues using Linux CLI — analyze logs, debug processes, and trace system behavior.
- Debug containerized environments (Docker, Kubernetes) — inspect workloads, networking, and resource utilization.
- Diagnose issues across distributed systems, VMs, and infrastructure layers.
- Conduct bug triage and root cause analysis with engineering teams.
- Integrate automated tests into CI/CD pipelines ensuring reliability and repeatability.
- Document test strategies, findings, and defects clearly and effectively.
Requirements
- 3–4 years of experience in QA with a strong focus on automation.
- Strong proficiency in Python (pytest, fixtures, parametrization, custom plugins).
- Hands-on experience with Playwright for UI and end-to-end testing.
- Strong Linux command-line expertise — log analysis, debugging, system inspection.
- Solid experience with Docker — containers, networking, volumes, and resource constraints (must-have).
- Proven ability in exploratory testing and uncovering non-obvious failure modes.
- Experience building test frameworks or harnesses from scratch.
- Familiarity with CI/CD pipelines and Git workflows.
- Strong understanding of QA methodologies, test planning, and coverage strategies.
- Excellent debugging, analytical, and problem-solving skills.
Nice to Have
- Exposure to Kubernetes (pod lifecycle, scheduling, kubectl usage).
- Experience with KVM/QEMU or hypervisor-level debugging.
- Background in testing distributed systems or cloud-native platforms.
- Experience with performance/load testing tools.
- Familiarity with GPU-based workloads or scheduling.