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Mid-level metro role with common QA title but niche AI/vector testing reduces applicant density.
Requires deep AI, vector DB, and platform QA expertise, limiting cross-industry transferability.
Explicit 6–10 years plus specialized AI/vector/LLM testing and mandatory automation skills enforce strict filtering.
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Design, develop, and execute automated test strategies focusing on APIs, vector databases, knowledge graphs, and LLM-based AI/data platforms.
Validate data integrity, performance, security (including multi-tenant RBAC and authentication) across enterprise AI stacks.
Collaborate with developers to identify edge cases, maintain test environments with synthetic data, and establish automated CI/CD quality gates.
6-10 years of QA engineering experience with backend services, APIs, and data platforms.
Bachelor’s or Master’s degree in Computer Science, IT, or related discipline.
Hands-on experience with test automation scripting using Python or Java.
Work Experience Required: 6-10 years in QA for relevant backend and AI/ML data platforms.
Expertise in testing AI/ML workloads including vector search systems and graph data architectures.
Experienced in building robust automated test frameworks and working within platform engineering or foundational infrastructure teams.
Strong familiarity with cutting-edge data and AI stacks (Neo4j, Milvus, LangChain) and cloud infrastructure (Microsoft Azure, Kubernetes, CI/CD pipelines).