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Remote role, metro location and common senior QA title increase density, offset by ML/graph specialization reducing applicant fit.
Core automation skills transfer broadly but graph database and ML/agentic testing require domain-specific experience.
Multiple mandatory technical skills (Python automation, CI/CD, graph DBs, ML testing) plus regulatory rigor enforce strict filters.
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Develop and maintain automated end-to-end and component test suites focusing on API and contract testing with browser automation tools.
Design and automate validation and regression testing for graph databases, graph analytics, AI/ML models, and agentic AI workflows, addressing probabilistic and non-deterministic outputs.
Embed quality gates in CI/CD pipelines with infrastructure-as-code provisioning for ephemeral test environments, managing test reliability metrics and collaborating closely with engineers for shift-left testing.
Bachelor’s Degree in Computer Science or related field.
Extensive hands-on experience in automated testing of web applications and services using Python and modern browser automation frameworks like Playwright or Selenium WebDriver.
Experience with testing REST APIs and web services, plus working knowledge of graph databases (Neo4j, Amazon Neptune, TigerGraph, JanusGraph) including query validation.
Familiarity with CI/CD pipelines, scripting beyond Python (e.g., Unix shell), and SQL; Work Experience Required: Proven extensive hands-on experience in relevant automated testing; Notice Period: Not explicitly mentioned in the JD.
Expertise in building and maintaining maintainable test architectures such as Page Object Model for complex, multi-layered platforms including probabilistic AI/ML components.
Experience integrating and validating AI/ML-backed features, agentic workflows, and graph analytics within a continuous integration and delivery cycle.
Comfortable working in cloud environments with infrastructure-as-code, automated environment provisioning, and managing testing reliability as a product metric.