Ravi Jadhav

Background

About Ravi Jadhav

• Skilled and versatile IT professional 17 years offering in the provision of performance testing and engineering solutions to small and large-scale enterprise products & Projects for Fortune 500 Clients. Have Rich and Proven Leadership experience in over 22 performance engineers working on Cloud & On-Premise model. • Specialization in consulting and actualizing large-scale POC’s for variety of clients with an understanding of execution testing/engg needs for On-Premise Cloud stage facilitated applications & give execution proposals on require premise. • Externally Certified in Load Runner v12.x Dynatrace BlazeMeter RPA Ui Path and Industry • Expert in PE Tool feasibility checks & implementations LoadRunner Blazemeter JMeter Splunk Dynatrace NeoLoad Keptn AWS CloudWatch FIDDLER RPA UI Path/Automation Anywhere & recommendations based on stakeholder for an application demand. • Profound knowledge of PTLC & in all phases of Performance Engineering lifecycle like NFR Elicitation & Analysis Defining Test Strategy Designing Test/Scripts Load Test Execution Result Analysis followed by Profiling & Tuning of an Application based on results. • Accomplice with Undertaking Design in adjusting execution testing to Dev Ops techniques including continuous delivery and platform as a service. Mastery in all testing types like Stress testing Load testing Capacity testing Soak testing etc. • Proficient in identifying bottlenecks like Slow Query Response Big Pure path sizing Pooling Issues High CPU consumption Security bypassing by entry requests Table Locking Thread Yielding Memory Leak this helped development teams to tune their application performance. • Expert in coining ‘workload models’ to suit the production usage to ensure that applications are tested for Performance and Scalability under production like scenarios • Successfully implemented chaos testing using Gremlin for validating production resilience in scenarios like POD termination and service failures. • Expertise in certifying the performance of Kafka-based microservices orchestrated within a Kubernetes environment and API performance testing.