Senior Performance & Resiliency Engineer - Cloud & SRE

JP Morgan Chase

Chicago (IL)

On-site

USD 130,000 - 180,000

Full time

11 days ago

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Job summary

JPMorganChase in Chicago is seeking a Software Engineer III to join an agile team, designing and delivering scalable, secure technology products for Consumer & Community Banking. You will contribute across multiple tech areas to support firm objectives.

The role emphasizes performance engineering, observability, resiliency, and safe releases, with hands-on coding, code reviews, and collaboration with AI-assisted development tools to improve quality, velocity, and reliability.

Qualifications

Formal training or certification on software engineering concepts and 5+ years applied experience.
Experience improving the performance and stability of critical, high-volume platforms.
Experience in performance engineering for distributed systems and/or SRE-style reliability engineering in production.
Strong cloud/container background (AWS + Kubernetes/EKS, ECS etc.).
Experience with modern observability tooling (e.g., Splunk, Datadog, Dynatrace, Grafana, OpenTelemetry, CloudWatch or equivalent).
Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment with the 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 considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls.
Ability to tackle design and functionality problems independently with little to no oversight
Practical cloud native experience

Responsibilities

Contribute performance engineering efforts: load/stress/soak testing, capacity modeling, performance tuning, and regression prevention (KPIs, guardrails, and acceptance criteria).
Improve production readiness: observability (metrics/logs/traces/APM), actionable alerting, incident triage, and root-cause analysis leading to durable remediation.
Apply resiliency patterns: timeouts/retries, circuit breakers, backpressure/rate limiting, graceful degradation, and failover readiness.
Support safer releases via canary/progressive delivery and automated rollback patterns.
Optimize containerized workloads across EKS (primary) and ECS/other compute where applicable; drive autoscaling strategy and right-sizing.
Develops secure and high-quality production code, and reviews and debugs code written by others
Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.