Lead SRE - Chase UK - Dublin

At JPMorganChase, we understand that customers seek exceptional value and a seamless experience from a trusted financial institution. That's why we launched Chase UK to transform digital banking with intuitive and enjoyable customer journeys. With a strong foundation of trust established by millions of customers in the US, we have been rapidly expanding our presence in the UK and soon across Europe. We have been building the bank of the future from the ground up, offering you the chance to join us and make a significant impact. 

As a Site Reliability Engineer at JPMorgan Chase within the International Consumer Bank, you will play a crucial role in this initiative, dedicated to delivering an outstanding banking experience to our customers. You will work in a collaborative environment as part of a diverse, inclusive, and geographically distributed team. We are seeking individuals with a curious mindset and a keen interest in new technology. Our engineers are naturally solution-oriented and possess an interest in the financial sector and focus on addressing our customer needs. We work in teams focused on improving the reliability, resilience, observability, and operability of customer-facing digital banking services. We build automation, define measurable reliability practices, reduce operational friction, and partner with engineering teams to ensure services are designed, delivered, and operated with reliability in mind.

Job responsibilities

• Own and drive continuous improvement of reliability, monitoring, and alerting across the service portfolio.
• Define and operationalise service-level indicators, service-level objectives, error budgets, user journeys, and reliability scorecards.
• Reduce operational toil by building automation, self-healing mechanisms, and scalable reliability tooling.
• Lead performance engineering and capacity planning, including load testing strategy, bottleneck analysis, and scaling plans.
• Establish resiliency patterns such as graceful degradation, timeouts, retries, circuit breakers, rate limiting, and failover strategies.
• Set technical direction and standards for observability, alerting, automation, and site reliability engineering practices.
• Provide hands-on implementation for complex or high-impact engineering work while delegating effectively to grow team capability.
• Partner across product, engineering, and platform teams to align reliability priorities with delivery roadmaps.
• Participate in feature planning and design reviews to ensure reliability values are built in from the start.
• Coach and mentor site reliability engineers by providing technical guidance, feedback, and support for development goals.
• Build practical AI capability within the team by encouraging effective practices, guardrails, validation, and safe usage patterns.
• 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.

Required qualifications, capabilities and skills

• Formal training or certification on software engineering concepts and advanced applied experience
• Proven experience as a software engineer, including proficiency in at least one programming language such as Python, Go, or Java.
• Demonstrated experience operating and improving production systems in a site reliability engineering or site reliability engineering capacity.
• Strong distributed-systems debugging and troubleshooting skills across services, infrastructure, and delivery pipelines.
• Experience with Kubernetes.
• Experience with cloud computing services.
• Familiarity with observability and reliability toolchains such as Grafana, Prometheus, Elasticsearch, Kibana, or Jaeger.
• Proven ability to lead technically by setting direction, making pragmatic trade-offs, guiding designs, and improving engineering standards.
• Strong communication skills with the ability to influence stakeholders and translate operational risk into clear engineering priorities.
• Ability to use AI-assisted engineering tools responsibly, including validating outputs, understanding failure modes, and adhering to secure handling practices.

Preferred qualifications, capabilities and skills

• Experience with AWS.
• Prior experience leading a site reliability engineering or site reliability engineering team, or acting as a technical lead for platform or production engineering.
• Experience building internal platforms or tooling, including operators, controllers, automation frameworks, or reliability guardrails.
• Experience applying AI to operational workflows such as alert enrichment, incident summarisation, or anomaly triage using approved tools and patterns.
• Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) 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.

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