SRE Software Engineer III

JPMorgan Chase & Co.•Jersey City, NJ

4d•$137,750 - $185,000

About The Position

As an SRE Software Engineer III at JPMorganChase within Corporate Technology, you will build and operate resilient software solutions that improve availability, scalability, and customer experience. You will collaborate across engineering and stakeholder groups to design automated delivery approaches, apply site reliability principles, and resolve complex issues before they impact users.

Requirements

Formal training or certification on software engineering concepts and 3+ years applied experience
Proficiency in site reliability culture and principles, including applying reliability practices within an application or platform
Proficiency in at least one programming language such as Python, Java/Spring Boot, or .NET
Experience building observability solutions, including telemetry collection and service level objective-based alerting using tools such as Grafana, Dynatrace, Prometheus, Datadog, or Splunk
Experience with continuous integration and continuous delivery tools such as Jenkins, GitLab, or Terraform
Familiarity with containers and orchestration technologies such as Docker, Kubernetes, or Amazon Elastic Container Service
Working knowledge of diagnosing and troubleshooting common networking concepts and issues in distributed systems
Ability to proactively remove blockers, learn new technologies, and apply new approaches to improve delivery outcomes
Working knowledge of using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.

Nice To Haves

Experience designing reliability improvements using error budgets, capacity planning, and resilience patterns (e.g., rate limiting, backpressure, graceful degradation)
Experience improving release safety with progressive delivery practices (e.g., canary deployments, feature flags, automated rollbacks)
Experience implementing automated quality gates (unit, integration, performance, and security testing) within delivery pipelines
Exposure to incident management practices, post-incident reviews, and implementing measurable remediation actions to prevent recurrence
Familiarity with infrastructure-as-code and standardized environment provisioning to improve consistency and auditability

Responsibilities

Design and implement automated continuous integration and continuous delivery pipelines to improve release quality, speed, and repeatability
Develop, test, and deliver software solutions that strengthen availability, reliability, scalability, and operational readiness of applications and services
Partner with engineers, technical experts, and key stakeholders to troubleshoot and resolve complex, multi-system problems to restore service and prevent recurrence
Define and use service level indicators and service level objectives to proactively identify risk, prioritize reliability improvements, and reduce customer impact
Advance observability practices by improving telemetry, dashboards, and alerting to shorten time-to-detect and time-to-recover
Improve operational excellence through runbooks, automation, and continuous improvement actions that reduce toil and production risk
Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
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.