Drive proactive performance engineering through early bottleneck detection, architectural guidance, capacity modeling, and runtime tuning
Build the tooling and infrastructure that turns performance testing into a scalable, self-service capability across teams
Design and maintain automated performance test suites (load, stress, soak, spike, capacity) with environment-aware, fully automated execution
Embed performance gates into CI/CD pipelines and build dashboards that correlate test signals with production telemetry against defined SLOs
Architects and governs agentic AI-enabled engineering workflows (using enterprise-authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation at scale
Required qualifications, capabilities, and skills
Extensive performance engineering experience (typically 12+ years) for high-traffic distributed systems—web, APIs, microservices, event-driven, and data-centric—built on a strong broader software engineering foundation
Deep hands-on expertise debugging performance problems in distributed systems, with the ability to go as deep as the system call boundary; strong knowledge of JVM internals
Fluency in both the Java (back-end) and Node.js/React (front-end) ecosystems, including front-end and browser-stack performance debugging best practices
Hands-on software engineering experience with Java/Spring Boot and Kubernetes (self-managed and EKS)
Strong systems and cloud performance depth, including Linux performance tooling, JVM tuning, containers, and OS-level troubleshooting under load
Deep expertise in workload modeling, queuing theory, and statistical analysis of latency and throughput; fluent with percentile-based SLOs and error budgets
Expert with at least one industry-standard load and protocol testing tool (e.g., JMeter, BlazeMeter, k6), with scripting and orchestration skills in Java, Python, or TypeScript for performance automation
Strong observability and APM capabilities using tools such as Dynatrace and/or OpenTelemetry, plus RUM and synthetic monitoring, with dashboarding in Kibana and/or Grafana
Proven ability to influence senior technical leaders, communicate complex performance trade-offs clearly, set engineering standards, and build tooling and practices that make high performance a scalable, self-service capability across teams
Demonstrated experience designing and leading adoption of agentic AI-enabled development practices (using enterprise-authorized tools within the work environment) across teams, including setting standards for human-in-the-loop validation, auditability/traceability of changes, and secure handling of sensitive data
Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications, data sensitivity, and risk-based governance; ability to influence senior technical leaders on safe scaling patterns and reuse
Preferred qualifications, capabilities, and skills
Experience operating in financial-services scale, low-latency systems, and/or regulated environments
Experience with data-platform performance optimization (e.g., Oracle tuning, JDBC pool tuning, Kafka throughput/partitioning, caching strategies)
Experience with service virtualization and fault injection tools (e.g., WireMock, Mountebank, Toxiproxy), including record-replay and dynamic templating
Familiarity with modern cloud-native load testing frameworks such as k6
Familiarity with service mesh technologies (Istio/Linkerd) and traffic-control patterns (rate limiting, backpressure)
Knowledge of advanced performance tooling (e.g., perf, eBPF) and production-grade troubleshooting practices
Practical application of LLMs for test generation, anomaly detection, or automated reporting in engineering workflows
This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorgan Chase’s review of criminal conviction history, including pretrial diversions or program entries.
