Load Test Engineer

1. Work with Product Owners Enterprise support and application/developer teams to review performance test requirements and translate them into performance test strategies.
2. Responsible for building Performance Test Plans defining and implementing standard methods and techniques of load stress testing for assigned systems.

3. Record performance tests with scalable number of Virtual Users (per requirements) to generate realistic load on the server using Jmeter or similar Load/Stress test tool. 4. Collect test metrics and prepare final Test reports Results for review and signoffs 5. Identify validate and report application usage metrics (like response times errors etc.) and identify unacceptable responses and provide recommendations to solve the same. 6. Build a suite of baseline and benchmark metrics for regular Release testing. 7.Work within project timelines effectively communication with QA Engineering teams and remain organized and detail oriented.

Requirements

1. At least 3 years hands on experience in planning and executing Load and Performance Stress test on across various Front end middleware like APIs and backend applications. 2. Comprehensive knowledge of system monitoring system programming and application servers. 3. Must have working knowledge to execute load and stress in JMeter.

4. Must have experience to write JMeter scripts to do performance testing of APIs Frontend and Backend applications. 5.Ability to understand business requirements (SLA response times) and build a performance test strategy to achieve successful test results. 6. Indepth knowledge of OpenSource Technologies code analysis and load testing tools 7. Must know how to build the Performance test reports for review and signoff. 8. Must have credible experience in Performance Testing on APIs/microservices. 9. Good English communication skills

Benefits

Standard Benefits

Key Responsibilities: 1. Develop and refine machine learning and deep learning models. 2. Apply expertise in neural network architectures, specifically for GenAI and LLM applications. 3. Handle complex data processing, cleaning, and visualization tasks. 4. Utilize natural language processing techniques for advanced AI solutions. 5. Efficiently deploy AI/ML models in production environments, focusing on scalability and robustness. 6. Uphold and enhance AI security measures to protect systems and data. 7. Collaborate with cross-functional teams to integrate AI solutions, particularly GenAI and LLMs, into broader systems and applications. 8. Stay abreast of the latest trends and advancements in AI, machine learning, GenAI, and LLMs. Technical Qualifications: 1. Proficiency in Python programming. 2. Advanced knowledge in mathematics and algorithm development. 3. Experience in developing machine learning and deep learning models. 4. Strong understanding of neural network architectures, with emphasis on GenAI and LLMs. 5. Skilled in data processing and visualization. 6. Experienced in natural language processing. 7. Knowledgeable in AI/ML deployment, DevOps practices, and cloud services. 8. In-depth understanding of AI security principles and practices.