My software-quality background is the foundation for how I now design AI evaluations, regression gates, observability, release controls, and failure analysis.
These metrics come from enterprise software work across manual testing, regression testing, feature validation, release support, test case creation, and automation contribution in a distributed delivery environment.
The work happened at Advisor360° — a cross-functional wealth-management / financial technology SaaS context — where quality engineering had to balance release readiness, defect evidence, test coverage, and changing product priorities.
512Bugs reported across feature testing, regressions, releases, and edge cases.
474Defects validated, retested, and moved through closure with reproducible evidence.
427QA execution, validation, documentation, and delivery work completed across tracked tickets.
99.3%Consistent story completion while supporting regression, release, and test documentation work.
70+Bitbucket commits connected to automation and technical QA contribution.
220New feature test cases authored — part of 447 created across the team during the tracked period.
Metrics are based on internal Jira reporting for the tracked review period.
Quality engineering teaches practical systems thinking: question assumptions, find edge cases, validate outputs, communicate risk, and protect users from hidden failures.
In AI and data work, the same discipline applies to source grounding, hallucination risk, data quality, leakage, bias, metric selection, prompt regression, observability, model monitoring, and rollback readiness.
Evidence-based examples of delivery ownership, traceability, and quality leadership.
Reported 512 defects and supported closure of 474 through feature testing, regression cycles, release validation, and clear reproduction evidence.
Authored 220 test cases out of 447 total new feature test cases, improving QA traceability, acceptance coverage, and clarity during development handoffs.
Worked with Python, Appium, and Sauce Labs and contributed 70+ Bitbucket commits while supporting regression testing, release validation, and technical QA workflows.
Translated technical evidence into clear reports that helped teams prioritize issues, validate fixes, and understand product stability.