Site Reliability Engineer III

There's nothing more exciting than being at the center of a rapidly growing field in technology and applying your skills to drive innovation and modernize the world's most complex and mission-critical systems. At JPMorganChase, you'll have the opportunity to work on platforms that move money globally — where reliability, security, and performance are not just goals, they are requirements. As a Site Reliability Engineer III at JPMorganChase within the Commercial & Investment Bank — Payments Technology team, you will solve complex and broad business problems with simple, straightforward solutions. Through code and cloud infrastructure, you will configure, maintain, monitor, and optimize highly available, low-latency payment processing platforms and their associated infrastructure. You are a significant contributor to your team, sharing your knowledge of end-to-end operations, availability, reliability, and scalability — and helping to raise the bar for the entire team. Job responsibilities Guide and assist teammates in building appropriate-level designs, gaining peer consensus, and supporting the adoption of site reliability engineering best practices across the team Collaborate with software engineers and cross-functional teams to design, develop, test, and implement deployment and reliability approaches using automated continuous integration and continuous delivery pipelines Implement infrastructure, configuration, and network as code for payment processing applications and platforms, ensuring consistency, repeatability, and auditability Monitor and maintain Kafka-based data ingestion pipelines, supporting the reliability and throughput of high-availability, low-latency payment flows Collaborate with technical experts, key stakeholders, and team members to resolve complex problems and proactively address issues using service level indicators and objectives before they impact customers Implement and improve observability solutions — including OpenTelemetry, Grafana, Splunk, and Dynatrace — to drive transparency, faster incident response, and measurable service level objective outcomes Apply enterprise-authorized AI capabilities to identify patterns in operational signals that indicate reliability risk or recurring toil, prioritizing reuse-first improvements tied to service level objective outcomes Use enterprise-authorized AI capabilities to accelerate incident triage, troubleshooting, and post-incident analysis, validating outputs and handling operational data in accordance with security and sensitivity requirements Proactively identify roadblocks and drive improvements to solve business problems, including exploring new technologies such as AI agentic models and MCP-based frameworks where appropriate Required qualifications, capabilities, and skills Formal training or certification on site reliability engineering concepts and 3+ years applied experience Proficiency in site reliability culture and principles, with familiarity in how to implement site reliability practices within an application or platform Proficiency in at least one programming language such as Python, Java/Spring Boot, or .NET Experience with observability practices such as white and black box monitoring, service level objective alerting, and telemetry collection Experience with continuous integration and continuous delivery tooling Proficient knowledge of software applications and technical processes within a given technical discipline such as cloud, AI, or distributed systems Familiarity with containers, container orchestration, and troubleshooting common networking technologies and issues Working knowledge of using enterprise-authorized AI capabilities to support site reliability engineering workflows, with strong validation habits and awareness of data sensitivity Ability to validate AI-assisted operational recommendations before applying changes, escalating when uncertain and adhering to data sensitivity requirements Preferred qualifications, capabilities, and skills Experience supporting highly available, secure, and low-latency payment processing platforms in a financial services environment, including familiarity with fraud screening or payment flow patterns Hands-on experience with Kafka and event-driven data ingestion pipelines, and working knowledge of AI agentic models and MCP-based frameworks applied to reliability use cases Proficiency with observability tooling such as OpenTelemetry, Grafana, Splunk, or Dynatrace, with a track record of improving reliability through monitoring, incident response, and automation Experience implementing service level objective programs, performance testing, capacity planning, and resilience testing including fault injection, chaos engineering, and disaster recovery exercises Experience improving operational maturity through standardized runbooks, automated health checks, auto-remediation, and deployment guardrails Familiarity with database reliability patterns including capacity management, backup strategies, and failover readiness, as well as secure operational practices such as least privilege and secrets handling