AI Performance Test Engineer

AI Performance Test Engineer
DevRev

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.