Senior Performance Engineering Lead for AI & Cloud Systems

symplr

United States

On-site

USD 130,000 - 190,000

Full time

14 days+

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Job summary

symplr is seeking a senior Performance Test Lead to design and steer scalable testing strategies for AI-enabled applications in the United States. You will lead load, stress, and capacity testing, build CI/CD integrated test frameworks, and mentor QA engineers to ensure reliability and performance.

The role requires 12+ years in performance testing, expertise with JMeter/LoadRunner/Gatling, and strong skills in Python/Java/JavaScript, cloud platforms, containers, and monitoring tools to identify

Qualifications

Bachelor’s or Master’s degree in computer science, Engineering, or a related field.
12+ years of experience in performance testing and engineering, with a strong understanding of methodologies and tools.
Proficiency in performance testing tools such as JMeter, LoadRunner, or Gatling.
Proficiency in programming languages such as Python, Java, JavaScript.
Extensive experience with cloud technologies and platforms (AWS, Azure, Google Cloud) and containerization (Docker/Kubernetes).
Strong understanding of web technologies and application architecture.
Experience in Application Monitoring Tools and profiling tools like Datadog, Dynatrace, Grafana, AppDynamics, Splunk.
Strong experience with CI/CD pipelines and DevOps practices.
Experience with search/streaming platforms like Elasticsearch, OpenSearch, Grafana, Kafka.
Hands-on experience with performance test simulations, analysis, and tuning in a microservices environment.
Hands-on experience in analyzing performance results across application, database, OS, and network.
Working knowledge of SQL and cloud databases like MongoDB, Cosmos DB, PostgreSQL.

Responsibilities

Design comprehensive performance testing strategies and lead initiatives to ensure system reliability, scalability, and responsiveness.
Conduct load, stress, and capacity testing to identify bottlenecks and areas for improvement.
Define KPIs, establish benchmarks, and build dashboards for real-time performance insights.
Architect scalable testing frameworks for performance and data validation with AI applications.
Lead troubleshooting of complex performance issues across QA, staging, pre-production, and production environments.
Mentor junior QA engineers and promote a culture of quality and continuous learning.
Integrate performance testing into CI/CD processes and guide team members on best practices.
Generate performance graphs, session reports, and documentation for validation and analysis.