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Remote role, common QA title, and Bangalore metro increase candidate density despite niche ML/graph needs.
Specialized graph, ML, and agentic testing skills limit transferability across industries.
Multiple mandatory technical skills and domain-specific ML/graph testing create strict candidate filters.
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Own the design, development, and maintenance of automated test suites for microservices, front ends, graph databases, and AI/ML systems, ensuring high test reliability and coverage.
Manage CI/CD quality gates and infrastructure-as-code for ephemeral test environments with synthetic data generation.
Develop validation harnesses for complex probabilistic AI/ML and graph analytics components, defining pass/fail criteria for non-deterministic outputs with regulatory compliance in mind.
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.
Working knowledge of graph databases (e.g., Neo4j, Amazon Neptune, TigerGraph, JanusGraph) including writing and validating graph queries.
Experience with CI/CD pipeline integration for automated testing (e.g., Jenkins, GitHub Actions, GitLab CI).
Experienced in maintaining scalable, reliable automated test architectures (e.g., Page Object Model) for end-to-end testing across UI, API, and contract layers.
Proficient in scripting (Python, Unix shell, Ruby) and familiar with testing probabilistic AI/ML systems requiring specialized assertion strategies.
Comfortable working with complex enterprise-grade platforms involving graph analytics, AI/ML workflows, and regulatory compliance contexts.