About the Role:

We’re looking for a Performance Test Engineer who thrives at the intersection of engineering precision and AI-driven innovation. In this role, you’ll design, execute, and evolve our performance testing ecosystem — blending the rigor of traditional testing with the intelligence of automation and agent orchestration.

You’ll work closely with engineering, DevOps, platform, and AI teams to simulate real-world conditions, identify bottlenecks, and ensure our systems are highly scalable, resilient, and efficient — delivering seamless experiences to millions of users globally.

What You’ll Do:

Core Performance Engineering

• Design and execute comprehensive performance test strategies — Load, Stress, Soak, Spike, Scalability, and Endurance testing.

• Develop and maintain performance scripts using tools such as JMeter, Gatling, LoadRunner, Locust, or k6.

• Simulate realistic user traffic and workload models for distributed systems.

• Perform root cause analysis across application, API, database, and infrastructure layers.

• Define and maintain performance baselines, SLAs, and SLOs.

• Integrate performance tests into CI/CD pipelines for continuous validation.

Advanced & AI-Driven Performance Testing

• Build AI-driven performance analysis frameworks using pattern recognition and anomaly detection.

• Develop custom test agents/orchestrators using MCPs to simulate large-scale, multi-node workloads.

• Implement self-healing test systems that adapt dynamically to environment changes.

• Use ML models to predict performance degradation and proactively optimize systems.

• Automate root cause detection with AI-assisted observability insights.

Monitoring & Insights

• Use observability tools (Grafana, Prometheus, Datadog, New Relic, AppDynamics) to monitor and analyze performance metrics.

• Create visual dashboards to communicate trends and optimization opportunities.

• Collaborate with SRE and development teams for end-to-end performance tuning.

Collaboration & Process

• Partner with engineering, QA, and platform teams early in the SDLC to define performance goals.

• Conduct post-release reviews and contribute to testing standards and best practices.

What You’ll Bring:

• 5+ years of experience in performance testing for large-scale distributed systems.

• Strong programming skills in Python, Go, or JavaScript/TypeScript.

• Hands-on with modern testing tools (JMeter, Gatling, Locust, k6, LoadRunner).

• Expertise in performance metrics — latency, throughput, error rate, concurrency, resource utilization.

• Experience integrating with CI/CD (Jenkins, GitHub Actions, Azure DevOps).

• Deep understanding of microservices, containers (Docker, Kubernetes), and distributed architectures.

• Skilled in analyzing logs, metrics, and traces for performance bottlenecks.

Bonus Points For

• Experience with AI-assisted testing, anomaly detection, or AIOps.

• Familiarity with chaos engineering tools (Gremlin, LitmusChaos).

• Exposure to AWS, Azure, or GCP environments.

• Database performance tuning and caching strategies.

• Contributions to open-source testing or AI performance frameworks.