Software Engineer III - Performance and Resiliency Engineering

JPMorganChase•Chicago, IL

About The Position

As a Software Engineer III at JPMorganChase within Consumer & Community Banking, you will serve as a seasoned member of an agile team, designing and delivering secure, stable, scalable technology products. You will provide critical technology solutions across technical areas and business functions in support of the firm’s business objectives, with a focus on performance and resiliency engineering. The role includes performance engineering, production readiness, application of resiliency patterns, safer release practices, optimization of containerized workloads, and development and review of secure, high-quality production code. You will also drive adoption and governance of approved AI-assisted engineering practices across teams, establish measurable validation standards, and promote reusable patterns and automation across the software development lifecycle toolchain.

Requirements

Formal training or certification in software engineering concepts and at least 5 years of applied experience.
Experience improving performance and stability of critical, high-volume platforms.
Experience in performance engineering for distributed systems and/or production SRE-style reliability engineering.
Strong cloud and container background, including AWS and Kubernetes/EKS, ECS, or similar.
Experience with modern observability tooling such as Splunk, Datadog, Dynatrace, Grafana, OpenTelemetry, CloudWatch, or equivalent.
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools for activities such as coding, code review, test acceleration, or troubleshooting.
Ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and adherence to resiliency and security expectations.
Experience coaching senior engineers or leads on compliant AI usage patterns and controls.
Ability to independently tackle design and functionality problems with little to no oversight.
Practical cloud-native experience.

Nice To Haves

Hands-on experience with load and performance tools such as JMeter, Gatling, or k6, and JVM profiling.
Experience with resiliency patterns and chaos or fault-injection testing.
Proficiency with observability tooling such as Prometheus, Grafana, and OpenTelemetry for baselines and capacity planning.

Responsibilities

Contribute to performance engineering efforts, including load, stress, and soak testing; capacity modeling; performance tuning; and regression prevention through KPIs, guardrails, and acceptance criteria.
Improve production readiness through observability, actionable alerting, incident triage, and root-cause analysis that leads to durable remediation.
Apply resiliency patterns such as timeouts, retries, circuit breakers, backpressure, rate limiting, graceful degradation, and failover readiness.
Support safer releases through canary or progressive delivery and automated rollback patterns.
Optimize containerized workloads across EKS and, where applicable, ECS or other compute environments; develop autoscaling strategies and right-size workloads.
Develop secure, high-quality production code and review and debug code written by others.
Drive adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes.
Establish measurable standards for validating AI-assisted work, including secure coding, peer review, and automated testing.
Promote reuse of proven patterns and automation within the SDLC/TLM toolchain.
Apply knowledge of software development lifecycle toolchain capabilities, including approved AI-assisted development and automation tools, to improve the value realized from automation at scale.