Performance Quality Assurance Engineer
3 weeks ago
About the job:
We are looking for an experienced AI Performance Tester to join our team.
The ideal candidate will have a strong background in testing AI applications and a deep understanding of AI testing parameters.
As an AI Performance Tester, you will be responsible for ensuring the quality and performance of our AI models by designing and executing comprehensive performance tests on Large Language Models (LLM), prompt engineering, and various AI testing methodologies.
About The Responsibilities:
- Design and execute performance tests for AI applications to measure accuracy, throughput, latency, and resource utilization.
- Develop test plans and test cases based on project requirements and AI testing parameters.
- Monitor and analyze test results to identify performance bottlenecks and areas for improvement.
- Work closely with developers and data scientists to understand the AI models and their performance requirements.
- Collaborate with cross-functional teams to ensure that performance testing is integrated into the development process.
- Provide feedback and recommendations to improve the performance and reliability of AI applications.
- Stay up-to-date with the latest trends and best practices in AI performance testing.
Role:
- Additionally, As an AI Performance Tester, your role involves assessing the performance, scalability, and efficiency of AI models and systems.
The following test parameters are crucial for ensuring the optimal functioning of AI applications under varying conditions:
Load Testing:
- Objective: Evaluate the AI model's performance under normal and peak load conditions.
- Key Metrics: Response time, throughput, and resource utilization.
- Scenarios: Simulate different user loads to identify system capacity and potential bottlenecks.
Scalability Testing:
- Objective: Determine how well the AI system scales with an increase in the volume of data or concurrent users.
- Key Metrics: Scalability factor, response time, and resource consumption.
- Scenarios: Test the system's ability to handle increased workloads without performance degradation.
Stress Testing:
- Objective: Assess the robustness of the AI model by subjecting it to extreme conditions and beyond its normal capacity.
- Key Metrics: System stability, error rates, and recovery time.
- Scenarios: Apply stress to the system by exceeding its designed capacity to identify breaking points.
Endurance Testing:
- Objective: Evaluate the system's performance over an extended period to identify memory leaks or performance degradation over time.
- Key Metrics: Memory usage, resource leaks, and response time.
- Scenarios: Run continuous tests for an extended duration to identify potential issues related to long-term usage.
Concurrency Testing:
- Objective: Determine how well the AI system handles multiple simultaneous requests and tasks.
- Key Metrics: Concurrency level, response time, and system stability.
- Scenarios: Simulate concurrent user activities and assess the impact on system responsiveness.
Resource Utilization Testing:
- Objective: Assess the utilization of system resources, such as CPU, memory, and disk space, during AI processing.
- Key Metrics: Resource consumption under varying workloads.
- Scenarios: Monitor resource usage during different AI tasks to ensure optimal utilization.
Algorithmic Efficiency Testing:
- Objective: Evaluate the efficiency of AI algorithms in terms of processing time and accuracy.
- Key Metrics: Execution time, accuracy, and algorithmic complexity.
- Scenarios: Analyze the performance of AI algorithms with different types and sizes of input data.
Batch Processing Testing:
- Objective: Assess the performance of AI models when processing large batches of data.
- Key Metrics: Batch processing time and resource consumption.
- Scenarios: Test the model's ability to handle bulk data processing efficiently.
Network Latency Testing:
- Objective: Assess the impact of network delays on the AI system's performance.
- Key Metrics: Network latency, data transfer rates, and system responsiveness.
- Scenarios: Introduce variable network latencies to observe their effect on AI processing.
Failover and Recovery Testing:
- Objective: Test the AI system's ability to recover gracefully from failures and disruptions.
- Key Metrics: Recovery time, data integrity, and system stability.
- Scenarios: Intentionally induce failures and assess the system's recovery mechanisms.
Relevant Qualifications:
- Bachelor's degree in computer science, Information Technology, or a related field.
- Master's degree is a plus.
- 7 years of experience in performance testing of AI applications.
- Strong understanding of AI testing parameters such as accuracy, throughput, latency, and resource utilization.
- Experience with performance testing tools such as JMeter, LoadRunner, or similar tools.
- Excellent analytical and problem-solving skills.
- Strong communication and collaboration skills.
- Familiarity with monitoring tools such as Splunk, Dynatrace, and Appdynamics for performance issue identification.
- Experience with Jenkins for managing CI/CD pipelines.
- Working knowledge of Postman, SoapUI, Bluezone, and mainframe skills
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