Systems Platform QA and AI engineer

This role drives end-to-end quality engineering for enterprise storage platforms within a large-scale hybrid cloud environment.
You will work across complex hardware and software layers, including firmware, BIOS, BMC/iLO, operating systems, drivers, storage protocols, and platform software.
The position combines advanced test automation, systems troubleshooting, and AI/ML techniques to solve challenging engineering and quality problems.
You will design scalable Python-based frameworks and reusable tools that improve validation, observability, productivity, and test coverage.
The role requires strong systems thinking and the ability to investigate dependencies across multiple technology layers.
You will contribute to innovation, continuous learning, collaboration, and knowledge sharing across engineering teams.
This is a full-time remote/teleworker opportunity based in Bengaluru, with meaningful scope to influence the reliability of enterprise technology.

• Bachelor’s or Master’s degree in Computer Science, Information Systems, or an equivalent technical discipline.
• 3–5 years of professional experience, with strong exposure to enterprise storage systems or comparable large-scale distributed systems.
• Strong Python programming skills, including practical experience developing automation frameworks, tools, test systems, or engineering utilities.
• Solid understanding of software testing fundamentals, debugging methodologies, automation design, and system validation.
• Good knowledge of Linux/Unix operating systems and the ability to troubleshoot issues within complex technical environments.
• Demonstrated experience resolving customer issues, performing system-level debugging, and collaborating effectively with cross-functional engineering teams.
• Strong understanding of enterprise storage environments and their hardware, firmware, driver, and platform software layers is highly desirable.
• Practical experience applying AI/ML techniques to engineering or quality challenges, including GenAI/LLMs, prompt engineering, AI-assisted testing, anomaly detection, or similar applications, is preferred.
• Strong problem-solving and analytical skills, with a QA and systems-thinking mindset focused on reliability, scalability, edge cases, and root cause analysis.
• Excellent collaboration and communication skills, with the ability to explain technical issues clearly and work effectively with diverse engineering stakeholders.
• Demonstrated interest and ability in mentoring, knowledge sharing, and supporting continuous improvement across the team.