Principal Test Automation Engineer

We are looking for a Principal Test Automation Engineer who combines deep storage domain expertise with the ability to build AI-native workflows that multiply engineering productivity, find bugs faster, and reduce escapes. You will set standards for AI-assisted validation workflows and scale best practices across the team. This role requires solid knowledge of SAS, SCSI, NVMe, NVMe-oF, and TCP, along with hands-on experience using AI to accelerate test creation, exploratory testing, triage, root cause analysis, and workflow automation. You will work across the full validation lifecycle — from requirements and user stories, to test planning and automation, to post-run triage, bug filing, documentation, and PR workflows — using tools like Jira and Jenkins, and MCP-enabled AI assistants. You won't just use existing tools; you'll build new ones where gaps exist. You'll join a team that is actively investing in AI-native engineering practices and with MCP integrations already connecting our AI workflows to tools like Jira and Jenkins, and a roadmap to expand further across our automation platform and REST APIs. You'll have the opportunity to shape how AI transforms our validation workflows, not just execute someone else's playbook. ESSENTIAL DUTIES AND RESPONSIBILITIES Translate requirements, design docs, and user stories into test plans, test cases, and pytest automation using AI-assisted and MCP-enabled workflows. Drive AI-assisted exploratory testing by identifying edge cases, negative scenarios, recurring failure patterns, and coverage gaps. Build and operationalize AI-driven triage, root cause analysis, and failure trend analysis — correlate logs, traces, firmware output, and hardware events across subsystems; build tools and dashboards that surface patterns, cut MTTR, and deliver data-backed release-readiness signals. Build reusable AI-driven workflows, templates, and playbooks across tools like Jira and Jenkins — including RAG-based knowledge systems grounded in platform specs, historical failures, and engineering documentation for test creation, triage, bug filing, documentation, execution summaries, and PR preparation. Mentor team members on AI-assisted engineering practices, prompt engineering, and MCP workflow adoption. Develop automated tests using pytest for firmware and storage subsystem validation across JBOD, JBOF, NAS, and SAN platforms. Optimize regression efficiency through risk-based test selection, rerun strategy improvements, prioritization, and flaky test detection and reduction. RQUIRED B.S. or M.S. in Computer Science, Electrical Engineering, or related field Experience converting requirements and user stories into test plans, test cases, and automated validation using AI-assisted workflows. Experience applying AI tools in engineering workflows, including promp engineering, RAG, and tool/agent integrations (e.g., MCP); production experience is a plus, but strong demonstrated initiative counts. Strong experience in test automation and quality assurance, with significant focus on storage systems. Hands-on experience with Python and pytest Strong understanding of storage systems and validation methodologies Knowledge of SAS, SCSI, NVMe, NVMe-oF Understanding of TCP Strong Linux troubleshooting and system-level debugging skills Good understanding of hardware-software interaction Experience identifying coverage gaps, weak scenarios, and missing negative tests, with understanding of regression optimization and data-driven quality improvement Strong communication, collaboration, and problem-solving skills Self-driven and able to work effectively in a fast-paced environment PREFERRED SKILLS Experience with AI-assisted IDEs (Windsurf, Cursor, Copilot) and custom workflow/rule authoring Active engagement with the AI landscape — you follow model releases, experiment with new tools, and bring relevant advances into your work. Experience testing REST APIs Experience with Fabric Switches ... (truncated, view full listing at source)