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Known cybersec brand and Bengaluru location increase applicant interest, but specialized AI/ML QA skillset limits generalist competition.
Specialized AI/ML and distributed-systems QA skills are transferable across cloud SaaS, though cybersecurity experience is beneficial.
Explicit 10+ years requirement plus specialized AI/ML, cloud, automation, and distributed-systems expertise increases filter strictness.
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Build, maintain, and extend automation frameworks and continuous integration/continuous delivery pipelines focused on AI, ML, distributed systems, and cloud-native services.
Architect and implement testing strategies including API validation, performance, load, fuzz testing, and AI model evaluation for quality, reliability, and scalability.
Collaborate across engineering teams to drive software quality improvements and maintain high-throughput distributed security systems processing over 600 billion events daily.
10+ years of experience delivering high-quality software solutions.
Experience with cloud platforms such as AWS, Azure, or GCP; working knowledge of distributed systems and microservices architectures.
Proficiency in automation frameworks, functional testing, load testing, fuzz testing tools (e.g., Locust, CATS), and continuous integration/continuous delivery pipelines.
Work Experience Required: 10+ years. Notice period: Not explicitly mentioned in the JD.
Experienced in AI/ML application testing with knowledge of large language model evaluation and prompt engineering concepts.
Strong background in building scalable testing infrastructure for distributed and cloud-native systems in cybersecurity or related domains.
Demonstrates deep expertise in automation-driven software quality assurance aligned with emerging AI, big data, and cybersecurity challenges.