Team Lead Site Reliability Engineer in Glasgow

Join a team where your engineering expertise directly protects the reliability and resilience of systems that matter at scale. At JPMorganChase, 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 JPMorganChase 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.
• Drive team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes.
• Apply 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.
• 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.
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment.
• 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 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.