QA and Test Engineering for Products That Cannot Afford a Bad Release

Test suites for AI-powered features: model outputs evaluated against ground truth, prompt robustness under adversarial inputs, and RAG accuracy tracked as reportable metrics.

Automated suites covering UI, API, and backend, architected for CI/CD with self-healing that adapts to interface changes. Flaky test detection ensures failures are deterministic.

Load, stress, spike, and endurance testing tied to defined thresholds, with chaos testing for distributed systems and AI-assisted anomaly detection during load runs.

Vulnerability scanning, VAPT, and security validation integrated into the QA cycle. DevSecOps-aligned, with AI-assisted prioritization ranking findings by exploitability.

Functional, integration, and consumer-driven contract testing across APIs and microservice boundaries, with breaking changes caught at the pull request stage.

Validation across browsers, operating systems, screen sizes, and device types against the product's target matrix, including native and cross-platform mobile.

WCAG 2.2 compliance validation combining automated scanning with manual assistive-technology testing, plus usability testing with real users against defined task flows.

Synthetic test data generation, production data masking, and environment data setup so test suites run against realistic data without exposing sensitive records.

Defect escape rate, coverage by feature area, automation reliability, and release readiness scores tracked across sprints and surfaced in leadership dashboards.