Performance Lead

Performance Lead with experience applying AI across the full performance engineering lifecycle. This role is responsible for ensuring the scalability, reliability, and efficiency of applications, platforms, and services through proactive performance planning, testing, monitoring, analysis, and optimization.

The ideal candidate combines deep expertise in traditional performance engineering practices with hands-on use of AI/ML-driven tools, automation, and analytics to improve test design, workload modeling, anomaly detection, root cause analysis, capacity forecasting, and continuous optimization.

Key Responsibilities

• Design and execute end-to-end performance engineering strategies for web, API, database, cloud, and distributed systems.
• Build and maintain performance test frameworks for load, stress, spike, endurance, and scalability testing.
• Use AI-enabled tools and methods to improve:
• test scenario generation
• workload modeling
• synthetic data generation
• test script creation and maintenance
• anomaly detection
• bottleneck identification
• root cause analysis
• predictive capacity planning
• Partner with development, QA, SRE, DevOps, product, and architecture teams to define performance requirements and non-functional acceptance criteria.
• Analyze application, infrastructure, database, and network behavior under load and provide actionable optimization recommendations.
• Integrate performance testing into CI/CD pipelines and support shift-left performance practices.
• Leverage observability platforms and AI-assisted analytics to correlate logs, metrics, traces, and events.
• Establish performance baselines, SLAs, SLOs, and KPIs for critical systems.
• Drive continuous improvement in performance engineering processes, tooling, governance, and automation.
• Mentor team members on both classical performance engineering techniques and emerging AI-assisted engineering practices.

Required Qualifications

• 8+ years of experience in performance testing/performance engineering.
• Strong experience with performance testing tools such as:
• JMeter
• k6
• BlazeMeter
• Hands-on experience with AI-assisted or AI-powered engineering tools used in performance analysis, test automation, observability, or operational intelligence.
• Strong understanding of:
• system architecture
• distributed systems
• microservices
• APIs
• databases
• caching
• messaging systems
• cloud-native platforms
• Experience analyzing performance metrics across application, infrastructure, and database layers.
• Proficiency in one or more scripting/programming languages such as Python, Java, JavaScript, Groovy, or Shell.
• Experience with monitoring/observability tools such as:
• Datadog
• Splunk
• Grafana
• AppDynamics
• Familiarity with CI/CD and DevOps tools
• Strong analytical, troubleshooting, and communication skills.