Software Engineer III

JPMorganChase•San Francisco, CA

About The Position

At JPMorgan Chase, the Site Reliability Engineer III works within the Enterprise Technology, Infrastructure Platforms team to modernize and support complex, mission-critical systems. The role configures, maintains, monitors, and optimizes applications and associated cloud infrastructure, independently improving existing solutions. The engineer contributes end-to-end operational knowledge and supports the availability, reliability, and scalability of the ServiceNow platform. The position owns ServiceNow platform reliability across incident response, service-level objectives and observability, automation, and performance engineering. It includes investigating and resolving platform issues, improving resilience and performance, tuning application and database layers, supporting upgrades, and partnering with engineering and architecture teams on remediation and platform standards. The role also uses enterprise-authorized AI capabilities to support incident analysis and identify operational reliability risks, while validating outputs and protecting sensitive data. Employment offers are contingent on JPMorganChase’s review of criminal conviction history, including pretrial diversions or program entries, pursuant to Section 19 of the Federal Deposit Insurance Act.

Requirements

Formal training or certification in site reliability engineering concepts and at least 3 years of applied experience.
Proficiency in site reliability culture and principles, including familiarity with implementing SRE within an application or platform.
Proficiency in at least one programming language such as Python, Java/Spring Boot, or .NET.
Strong enterprise platform support experience and practical SRE and observability fundamentals relevant to ServiceNow.
At least 10 years of experience in technical support, software development, performance load testing, or professional services.
Expert-level understanding of web application stack components.
Experience scripting in one or more of JavaScript, Python, Perl, Unix shell, or Windows PowerShell.
Experience working with and debugging object-oriented code such as Java.
Strong RDBMS experience with Oracle, MySQL, SQL Server, or PostgreSQL, including performance tuning.
Experience with JVM performance tuning, including memory and garbage collection, and troubleshooting complex systems.
Working knowledge of enterprise-authorized AI capabilities for SRE workflows, with strong validation habits and awareness of data sensitivity.
Ability to validate AI-assisted operational recommendations before applying changes, escalate when uncertain, and follow data sensitivity requirements.

Nice To Haves

ServiceNow CSA certification or higher.
Prior ServiceNow architecture exposure; training can be provided.
Cloud or SaaS experience.
Memory management and dump analysis, preferably Java heap dump analysis.
ITSM, ITIL, or CMDB fundamentals.
Linux/Unix or Windows Server administration.
Browser or network troubleshooting tools such as Firebug, Chrome DevTools, or Fiddler.

Responsibilities

Own end-to-end reliability for the ServiceNow platform, including incident response, SLOs and observability, automation, and platform performance engineering.
Troubleshoot incidents and problems, lead blameless post-mortems, and drive actions to prevent recurrence.
Define and defend SLIs, SLOs, and error budgets; improve telemetry, monitoring, alerting, and performance-testing review.
Monitor platform and product performance metrics and partner with engineering teams on remediation.
Identify and deliver stability, resiliency, and performance improvements, and support platform and vendor upgrades.
Automate repetitive operational work to reduce toil.
Perform design reviews for platform and tenant implementations and partner with architecture on platform standards.
Analyze ServiceNow instances across the stack to identify and remediate resource contention; examine platform Java and JavaScript to understand application behavior.
Improve throughput through JVM memory allocation and garbage-collection tuning, and optimize relational database performance through query refactoring or database tuning.
Use enterprise-authorized AI capabilities to accelerate incident triage, troubleshooting, and post-incident analysis; validate outputs and handle operational data according to sensitivity and security requirements.
Use enterprise-authorized AI capabilities to identify reliability risks and recurring toil in operational signals, prioritizing reuse-first improvements tied to SLO outcomes.