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Tier-1 brand and Bangalore increase competition; storage QA specialization moderates density.
Role demands niche storage platform and system-level QA expertise, reducing cross-industry transferability.
Explicit 6–10 years plus domain-specific storage QA and Python automation requirements make filters strict.
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Lead quality engineering for enterprise storage platforms covering hardware-software interactions including firmware, drivers, and system software.
Design and implement scalable test automation frameworks and tools using Python to enhance test coverage and debugging efficiency.
Conduct deep root cause analysis of complex customer issues and apply AI/ML techniques such as Generative AI for failure analysis, anomaly detection, and workflow automation.
6–10 years of experience in QA, system engineering, or storage systems, preferably in enterprise or distributed environments.
Strong programming skills in Python or Perl with experience building automation frameworks and tools.
Solid understanding of Linux/Unix systems, system-level troubleshooting, and debugging methodologies.
Familiarity with enterprise storage technologies including hardware, firmware, drivers, and platform software layers.
Experienced in driving system-level testing strategies focused on performance, reliability, scalability, and edge cases in storage platforms.
Skilled in leveraging AI/ML including Generative AI and LLMs for test optimization and automation in complex engineering environments.
Capable of cross-team collaboration and mentoring to improve product quality and foster engineering excellence.