Designed and built an internal end-to-end QA orchestration platform using React + Redux + Node.js + Express + WebSockets + Playwright + OpenAI API. Enables teams to define reusable test flows, execute against live environments, and automatically compare results against versioned baselines. Fully deployed and in active use. Developed AI-powered test case generation from user stories using GPT-4o, producing structured test plans mapped to team documentation format, reducing manual review time and improving decision accuracy. Led QA for over 3 years, covering regression testing, flow validation, end-to-end test case design, and release sign-off for critical FinTech systems, serving as primary QA authority across FX and securities platforms. Automated QA pipelines and operational workflows in Python, significantly reducing manual effort and improving test reliability and repeatability, with REST API testing integrated across all major flows. Resolved high-impact incidents across multiple financial systems (N2/N3 production support), provided root-cause analysis, and ensured rapid recovery with minimal business disruption. Reviewed and validated approximately 300 SWIFT payment messages per day (MT103, XML ISO 20022), ensuring regulatory compliance and operational accuracy. Implemented monitoring and alerting via Grafana for real-time system health and data availability tracking. The Modular QA Platform is an internally-developed platform that redefines how QA is performed in FinTech environments, moving from isolated, manual checks toward structured, traceable, and AI-augmented validation. Its architecture includes a React + Redux frontend with WebSocket-driven real-time execution and a Node.js + Express event-driven backend for orchestration, validation, and result management. The core capability allows defining reusable test flows with selectable system, process, inputs, and steps, executing flows, and comparing live outputs against stored baselines to detect regressions automatically, utilizing Playwright for the full E2E automation layer. Advanced features include inter-system pipelines with dependency chaining (output of flow A feeds input of flow B), dynamic input handling (e.g., live 2FA), and execution progress tracking. The AI layer provides AI-powered test case generation from user stories using GPT-4o, producing structured test plans mapped to team documentation format, and intelligent failure diagnosis to support faster incident resolution. This platform reduces manual regression effort, improves release confidence, and creates an auditable QA record across financial system changes.