Systems Platform QA and AI engineer

Lead end-to-end quality engineering for enterprise storage platforms, focusing on hardware-software interactions including firmware, BIOS, operating systems, and network components.

Design and develop scalable Python-based automation frameworks and tools to improve test coverage, system validation, debugging, and productivity.

Apply AI/ML techniques to engineering quality problems such as failure analysis, test optimization, anomaly detection, and workflow automation.

Bachelor’s or Master’s degree in Computer Science, Information Systems, or equivalent.

3-5 years of experience with strong exposure to enterprise storage systems or similar large-scale distributed systems.

Strong programming skills in Python and solid understanding of testing, debugging, and automation design.

Good understanding of Linux/Unix operating systems; demonstrated experience in customer issue resolution and cross-functional engineering collaboration.

Experience with enterprise storage systems across hardware, firmware, drivers, and platform software layers.

Proven ability in building automation frameworks and applying AI/ML techniques to solve complex engineering or quality assurance challenges.

Strong troubleshooting and system thinking approach focused on reliability, scalability, and edge conditions in large-scale enterprise environments.