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Mid-level QA in metro with broad skill requirements and common title increases candidate competition.
Specialized AI and cloud QA skills reduce cross-industry transferability moderately.
Explicit 6–9 years plus mandatory AI, cloud, programming, and tooling requirements enforce strict shortlisting.
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Own the quality assurance of Agentic AI systems that autonomously manage cloud infrastructure across AWS, Azure, or GCP.
Design, develop, and maintain scalable AI-driven test automation frameworks (using Python or Java) to validate LLM behaviors, API reliability, and secure cloud orchestration workflows.
Integrate intelligent, self-healing test suites into CI/CD pipelines for zero-touch deployments and collaborate with Data and Cloud Engineers to create synthetic datasets for robust AI testing.
6-9 years experience in software test automation, including at least 1 year testing AI/ML applications or MLOps pipelines.
Strong working knowledge of AWS, Microsoft Azure, or Google Cloud Platform (GCP) operations and APIs.
Advanced proficiency in Python, Java, or Go for custom test script development.
Familiarity with AI/ML concepts such as NLP, model evaluation metrics, and reinforcement learning.
Experienced in testing complex AI/ML systems with focus on LLM validation, MCP, connectivity, guardrail, A2A, request handler, and orchestrator agent testing.
Capable of building and integrating AI-driven automated test frameworks that support autonomous cloud resource management workflows.
Comfortable working in cloud-native, AI-automated environments requiring collaboration with cross-functional engineering teams and CI/CD integration.