Implement and maintain reliable production systems: design and deliver small-to-medium projects, write maintainable code, triage and resolve incidents, reduce operational toil via automation, and improve observability and SLO/SLA monitoring while responsibly using enterprise AI capabilities.
Play a key role in ensuring system reliability at one of the world’s most iconic and largest financial institutions.
As a Site Reliability Engineer II at JPMorgan Chase within the Commercial & Investment Bank Payments Technology team, you will use technology to solve business problems and leverage software engineering best practices as we strive towards excellence. This role often works independently to execute small to medium projects, but you’ll also have the opportunity to collaborate with cross functional teams to continually improve your level of knowledge about JPMorgan Chase’s business and relevant technologies.
Job responsibilities
- Executes small to medium projects independently with initial direction and eventually graduates to designing and delivering projects by yourself
- Leverages technology to solve business problems by writing high quality, maintainable, and robust code following best practices in software engineering
- Participates in triaging, examining, diagnosing, and resolving incidents and work with others to solve problems at their root
- Recognizes the toil within your role and proactively works towards eliminating it through either systems engineering or updating application code
- Understands observability patterns and strives to implement and improve service level indicators, objectives monitoring, and alerting solutions for optimal transparency and analysis
- Uses enterprise-authorized AI capabilities within the work environment to speed up incident triage, troubleshooting, and post-incident analysis, validating outputs and handling operational data according to sensitivity and security requirements
- Applies enterprise-authorized AI capabilities within the work environment to identify recurring toil and reliability risks from operational signals, prioritizing reuse-first improvements and measurable SLO outcomes
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 2+ years applied experience
- Proficient in site reliability culture and principles and familiarity with how to implement site reliability within an application or platform, including strong understanding of SLI/SLO/SLA and error budgets
- Experience in observability such as white and black box monitoring, service level objective alerting, and telemetry collection using tools such as Grafana, Dynatrace, Geneos, Prometheus, Datadog, Splunk, and others
- Proficient knowledge of software applications and technical processes within a given technical discipline (e.g., Cloud, AI, Android, etc.), including hands-on experience in system design, resiliency, testing, operational stability, and disaster recovery
- Strong expertise in AWS services, including EC2, S3, RDS, VPC, IAM, and networking.
- Proficient in at least one programming language such as Python, Java/Spring Boot for AI/ML modeling and automation to reduce operational toil by building tools for repeated tasks
- Experience with continuous integration and continuous delivery tooling, along with experience running production incident calls and managing incident resolution in collaboration with cross-functional teams
- Experience with continuous integration and continuous delivery tools like Jenkins, GitLab, or Terraform
- Practical cloud experience in AWS supporting production services (visibility/troubleshooting, deployments, and operational hygiene) and strong debugging fundamentals across distributed systems (logs/metrics, latency analysis, dependency failures, data issues)
- Working knowledge of using enterprise-authorized AI capabilities within the work environment to support SRE workflows (e.g., troubleshooting support and runbook drafting) with strong validation habits and awareness of data sensitivity. Ability to assess AI-assisted operational recommendations for correctness and risk, and apply appropriate controls to maintain resiliency, security, and auditability
Preferred qualifications, capabilities, and skills
- Experience with continuous integration and continuous delivery tooling
- Experience applying AI-assisted tooling to reduce support toil
- Familiarity with container and container orchestration and troubleshooting common networking technologies and issues
- Ability to identify new technologies and relevant solutions to ensure design constraints are met by the software team
- Ability to initiate and implement ideas to solve business problems
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