Systems Platform QA and AI engineer

Team: IT

Location: India

Commitment: Full-time

Workplace Type: remote

Accountabilities:

• Drive end-to-end quality engineering for enterprise storage platforms, validating interactions between hardware, firmware, BIOS, BMC/iLO, operating systems, kernels, device drivers, storage protocols, backend networks, and platform software.
• Design, develop, and maintain scalable automation frameworks and engineering tools, primarily using Python, to increase test coverage, validation effectiveness, debugging capabilities, and overall productivity.
• Troubleshoot complex issues across system layers and dependencies within large-scale enterprise storage environments, conducting root cause analysis and supporting timely resolution.
• Apply AI and machine learning approaches to engineering and quality challenges, including failure analysis, test optimization, anomaly detection, workflow automation, generative AI, and AI-assisted testing.
• Build reusable tools and utilities that improve engineering productivity, test effectiveness, system observability, and the ability to diagnose complex platform behaviors.
• Collaborate with cross-functional engineering teams to resolve customer issues, improve system reliability, and address quality challenges throughout the development and validation lifecycle.
• Promote innovation, continuous learning, collaboration, mentoring, and knowledge sharing while contributing to improvements in engineering practices and quality processes.

Requirements

• 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.

Benefits

• Remote/teleworker work arrangement, primarily working from home.
• Comprehensive benefits designed to support physical, financial, and emotional health and well-being.
• Access to personal and professional development programs supporting career growth and opportunities to deepen expertise or explore other areas.
• An inclusive workplace that values individual backgrounds, perspectives, and contributions.
• Opportunities to work on innovative enterprise cloud and storage technologies with complex engineering challenges.
• A collaborative environment that encourages continuous learning, mentoring, knowledge sharing, and innovation.
• Opportunities to develop expertise in AI/ML applications, automation, systems quality engineering, and large-scale enterprise platforms.