ABDUL MUNAFF

Designed and owned enterprise-wide performance test strategies for a large-scale social media and payments ecosystem serving 100M+ users—defining load profiles, concurrency models, soak test schedules, and scalability thresholds aligned to SLA targets and business growth projections. Established performance test frameworks that validated high-volume content ingestion and ad delivery pipelines under peak load conditions — simulating real-world traffic patterns including ramp-up, sustained load, spike, and stress scenarios. Personally executed all critical performance test cycles, analysing throughput, response time percentiles (p50/p95/p99), error rates, and resource saturation curves — translating raw metrics into actionable engineering recommendations. Drove shift-left performance engineering by embedding performance acceptance criteria into sprint planning and user story definitions, ensuring non-functional requirements were validated before features reached integration testing

Architected enterprise-wide automation frameworks using Selenium, Appium, TestNG, and Cucumber, enabling unified testing across FinTech and API platforms for 50M+ daily active users. Led development of GenAI “Mega Prompts” to autogenerate BDD scenarios and automation-ready artifacts — reduced test planning time by 62.5%. Built ML-assisted utilities for Predictive Quality Coverage, cutting test authoring effort by 40% while improving defect detection rates. Implemented CI/CD quality gates and shift-left strategies that accelerated release cadence by 40% across multiple product lines. Designed and maintained scalable API validation frameworks to ensure data integrity in a microservices architecture. Established performance engineering practices (load and stress testing) to validate high-throughput systems and maintain SLA targets. Led cross-functional QA teams in adopting AI-augmented workflows and introduced process metrics to measure improvements in test efficiency and cycle time

Owned performance and load testing as a primary responsibility for a large-scale SaaS AdTech platform — executing load, stress, soak, and spike tests using JMeter and LoadRunner across critical application tiers. Designed realistic load models simulating concurrent campaign delivery, reporting queries, and user-facing transactions — identifying database bottlenecks, connection pool exhaustion, and API response degradation under peak load conditions. Produced detailed performance analysis reports (throughput, latency distributions, error trends, and server resource utilization) used by architects for tuning decisions. Validated platform scalability before major releases, confirming the system could sustain high traffic during campaigns. Collaborated with DBAs and backend engineers to resolve performance defects and performed API/web service validation.