Employer description & source details
Design, develop, and execute performance tests for web, API, and microservices-based applications. Create and maintain performance test scripts using JMeter, LoadRunner , or similar tools. Support workload modeling, performance benchmarking, and capacity testing activities. Analyze test results and identify application, database, or infrastructure bottlenecks. Develop automation utilities and reusable components using Python etc. Utilize observability and monitoring platforms to investigate performance issues. Collaborate with development teams to troubleshoot and optimize system performance. Support AI-assisted initiatives such as test generation, analysis automation, and reporting enhancements. Contribute to continuous improvement of performance engineering frameworks and best practices. Solid experience in Performance Testing or Performance Engineering. Hands-on experience with JMeter, LoadRunner, VTS , or equivalent tools. Understanding of APIs, microservices, REST services, and distributed systems. Familiarity with Kubernetes , Linux, and cloud environments. Exposure to observability/APM solutions such as Datadog, Dynatrace, Coralogix, New Relic, or AppDynamics . Basic understanding of Generative AI tools such as GitHub Copilot or Azure OpenAI . Strong analytical and troubleshooting skills. Should have 3-5 years of strong experience with CI/CD pipelines and GitHub Actions . Exposure to log analytics platforms such as Splunk or Coralogix . Knowledge of performance testing within cloud-native architectures. Familiarity with AI-assisted engineering workflows. ✅ Strong foundation in performance testing and automation ✅ Understanding of cloud-native applications and Kubernetes ✅ Passion for leveraging AI and modern engineering practices ✅ Strong analytical and problem-s
Extracted skills: python, azure, kubernetes, artificial intelligence, operations