Lead Site Reliability Engineer – Operations Excellence in Glasgow

Description

Join a team where your engineering expertise directly protects the reliability and resilience of systems that matter at scale.

At JPMorgan Chase, we invest in engineers who think beyond the code — who own outcomes, drive operational excellence, and raise the bar for how technology performs under pressure.

As a Lead Software Engineer focused on Site Reliability and Operations Excellence, you will have the opportunity to shape how we respond to, learn from, and prevent incidents across a complex, high-stakes technology environment.

This is a role where your impact is visible, your voice carries weight, and your work directly influences the stability of services relied upon by millions.

As a Lead Software Engineer - Site Reliability Engineer, Operations Excellence at JPMorgan Chase within the AI/ML & Data Platforms area, you will serve as a technical leader at the intersection of software engineering and reliability engineering — owning the incident management lifecycle, defining reliability standards, and partnering across Engineering, Product, Infrastructure, and Security to deliver durable operational improvements.

You will bring structure to complexity, clarity to high-urgency situations, and a continuous improvement mindset to everything from alert quality to executive reporting.

Your work will directly strengthen the firm's ability to detect, respond to, and prevent production issues at scale.

• Job Responsibilities
• Own and continuously improve the incident management lifecycle, including triage, escalation, stakeholder communications, and recovery, ensuring consistent execution and measurable improvement over time.
• Serve as Incident Commander for major incidents, coaching responders to follow defined processes and site reliability engineering best practices while maintaining clear and timely stakeholder communication.
• Drive operational readiness through drills, game days, and failure-mode exercises that improve response effectiveness, surface gaps, and build team resilience before incidents occur.
• Define, implement, and enforce reliability standards across services, including service level indicators and objectives, error budgets, monitoring coverage, and alert quality.
• Strengthen change and release management practices by establishing readiness checks, progressive delivery standards, rollback procedures, runbook quality, and production hygiene expectations.
• Partner with Engineering, Product, Infrastructure, and Security teams to prioritize reliability work and deliver solutions that address user and operational pain points with lasting impact.
• Lead problem management and root cause analysis by debugging complex production issues, identifying systemic root causes, and driving durable remediation and preventative actions.
• Own reliability reporting and operational governance by tracking key performance indicators — including availability versus service level objectives, mean time to detect and recover, incident trends, alert noise, and change failure rate — and producing executive-ready summaries.
• Facilitate operational forums including operations reviews, incident review boards, and reliability councils to drive accountability, share learnings, and align stakeholders on priorities.
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e. g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• 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.
• Required qualifications, capabilities, and skills
• Formal training or certification on software engineering concepts and advanced applied experience.
• Demonstrated experience improving operational processes, including incident management, root cause analysis, problem management, and operational governance in complex production environments.
• Strong knowledge of reliability engineering concepts including service level objectives and indicators, error budgets, capacity planning, resilience patterns, and observability.
• Proven ability to influence across teams, drive cross-functional initiatives, and communicate clearly with stakeholders — particularly in high-urgency, time-sensitive situations.
• Strong analytical and reporting skills with the ability to define metrics, generate actionable insights, and drive decisions based on operational data.
• Hands-on experience with cloud platforms such as Amazon Web Services, Google Cloud Platform, or Microsoft Azure, and infrastructure-as-code tooling including Terraform.
• Systematic problem-solving and troubleshooting skills applied to complex, distributed systems in production environments.
• Demonstrated ability to operate with a high degree of ownership, self-direction, and urgency in ambiguous or fast-moving situations.
• Hands-on experience 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.
• Preferred qualifications, capabilities, and skills
• Experience leading reliability programs across multiple services or teams at a platform or domain level.
• Experience building automation for operations, including runbook automation, event correlation, alert tuning, and self-healing patterns.
• Exposure to agentic AI or AI-driven operations concepts, including workflow orchestration, decision support, and safe automation patterns.
• Familiarity with operational governance frameworks and experience facilitating reliability forums or review boards at scale.