Performance Test Engineer

Acesoft Labs; Gurugram District; On-site; INR 1,800,000 - 3,500,000; Full time

Job summary

Acesoft Labs in Gurugram is seeking a Sr. Performance Test Engineer with 8+ years of hands-on experience to strengthen our performance testing efforts.

You will design and execute JMeter load tests, and write Beanshell/Groovy/JavaScript scripts for RestAPI and web testing. You will interpret NFRs, analyze results, identify bottlenecks across client, server, and network layers, and collaborate with developers to optimize performance.

Qualifications

• 8+ years of professional experience in performance testing.
• Proficient in designing and executing load tests with JMeter and scripting within JMeter.
• Experience interpreting NFRs and testing across web, API, microservices and cloud architectures.

Responsibilities

• Analyze test results and identify bottlenecks across client, server, network layers.
• Collaborate with developers/architects to optimize system performance.
• Maintain and enhance CI/CD/automation for performance testing.

Skills

Performance testing

JMeter scripting

Beanshell scripting

Groovy scripting

JavaScript scripting

REST/HTTP testing

NFR understanding

System architecture

Test automation

Bottleneck analysis

Tools

Jenkins

Azure DevOps

GitHub

Docker

Kubernetes

Grafana

Prometheus

AWS CloudWatch

Job description

We are looking for a highly skilled Sr. Performance Test Engineer with over 8 years of experience to join our team. The ideal candidate will have extensive experience in performance testing and a strong ability to write and execute scripts using JMeter(5year into Jmeter).

• Must-Have Skills:
• Strong ability to write Beanshell / Groovy / JavaScript scripts within JMeter, HTTP/HTTPS protocols including RestAPI and web(front-end) based performance test scripting
• Ability to interpret NFRs, understanding system architecture (web, API, microservices, cloud), create the testbed for automated test execution and reporting
• Strong skills in analyzing test results, identifying bottlenecks across app layers - client, server, network & AWS DocDB
• Ability to troubleshoot issues and collaborate with Developers/ Architects to optimize system performance
• Good-to-Have Skills:
• Understanding of CI/CD tools (Jenkins/ Azure DevOps, GitHub, AWS ECS), familiarity with Containers (Docker/ Kubernetes), and AI-assisted QA tools (Qodex.ai, LoadMind AI Analyzer, etc.)
• Familiarity with monitoring & observability tools such as Grafana/ InfluxDB/ Prometheus and AWS - CloudWatch, Performance Insights, CloudTrail, etc.