Case 01
Professional system
BluWave AI
Quality architecture for AI-powered energy products
At BluWave AI, I own the testing approach across web and mobile interfaces, APIs, backend services, data workflows, and forecasting behaviour.
Context
A growing product surface needed more than isolated automated checks. It needed coherent quality foundations built from first principles: useful coverage, reliable environments, readable evidence, and methods suited to both deterministic software and model-driven behaviour.
Response
- Architected dedicated automation foundations for UI and mobile testing with WebdriverIO, Appium, and Mocha; API regression with Bruno and Postman/Newman; SOAP/XML services; and performance, load, and soak work with k6 and Locust.
- Integrated suites into Dockerized Jenkins pipelines with environment resolution, secure configuration, normalized reporting, and Jira/Xray result publishing.
- Designed risk-based API coverage across authentication, access control, CRUD workflows, filtering, persistence, schemas, status behaviour, negative paths, and deterministic cleanup.
- Validated forecasting quality through scenario-based testing and measures including MAE, RMSE, MAPE, correlation, bias, peak miss, and lag, connecting statistical behaviour to product expectations.
- Traced failures across logs, API responses, dashboards, devices, data, and CI evidence to give developers and product owners a clear engineering signal.