Lead Site Reliability Engineer

Assume a critical role in defining the future of a globally recognized firm and have a direct and significant effect in a realm tailored for top achievers in site reliability.

As a Lead Site Reliability Engineer at JPMorgan Chase within the Infrastructure Platforms team, you hold a leadership role in your team, demonstrate strong knowledge across multiple technical domains, and advise others on the technical and business issues facing them. Take lead and conduct resiliency design reviews, break up complex problems into digestible work for other engineers, act as a technical lead for medium to large-sized products, and provide advice and mentoring to other engineers.

Job responsibilities

• Engineer reliability into enterprise-scale platforms by writing production software: automation, control loops, self-healing, and tooling that remove manual operations rather than institutionalise them
• Build systems around declarative, intent-based design: model the desired state as data in a trusted source of truth and let automation continuously reconcile reality to it, rather than driving change through imperative, one-off scripts
• Treat data as a first-class reliability asset: instrument, collect, and reason over telemetry and state data to drive detection, diagnosis, and closed-loop remediation
• Define and operationalize SLIs and SLOs with stakeholders; implement SLO-based alerting, telemetry standards, and actionable observability
• Own services end-to-end, taking accountability for reliability, performance, security, and cost, and building operability and observability in from the start
• Share an on-call rotation and act as a technical leader during major incidents: drive triage, mitigation, communications, and blameless post-incident reviews, then engineer the durable fix
• Drive down toil measurably through automation and better engineering; treat repeated manual work as a bug to be coded out
• Work AI-native across the SDLC (AI-assisted development, code review, test generation, incident and root-cause analysis) with clear validation standards (secure coding, peer review, automated testing), so speed never compromises correctness
• Decompose ambiguous reliability problems into clear, executable work for yourself and for AI agents, and integrate the results into coherent, production-ready systems; set reliability standards and raise the engineering bar across your team and partner organizations; apply security and operational-risk judgment throughout the engineering lifecycle
• Leads reuse-first adoption of AI-assisted reliability workflows across SDLC/toolchain practices (e.g., CI/CD quality checks, test/validation automation, and operational readiness), ensuring traceability/auditability, resiliency, and security controls.
• Uses enterprise-authorized AI capabilities within the work environment to accelerate major-incident triage, troubleshooting, and post-incident analysis, validating outputs and handling operational data according to sensitivity and security requirements.

Required qualifications, capabilities, and skills

• Formal training or certification on site reliability engineering concepts and 5+ years applied experience
• Strong production coding skills in an industry-standard language (e.g. Python, Go, Java, C++, Rust); this is a software engineering role
• Hands-on experience operating production systems at scale, including on-call ownership, incident response, and designing for reliability/operability
• Practical SLI/SLO/error-budget experience (or clear aptitude and appetite to own and evolve it)
• Deep observability experience: white-box/black-box monitoring, SLO-based alerting, and telemetry using tools such as Grafana, Prometheus, Splunk, Datadog, Dynatrace, or equivalent
• Proficiency with *nix plus infrastructure automation and tooling (e.g. Kubernetes, Terraform, CI/CD)
• Strong systems thinking across interfaces, contracts, failure modes, and interactions at scale
• Fluency directing AI tools to do real engineering work (beyond autocomplete), with sound judgment on when AI applies vs. when deep human expertise is required
• Security-first and outcome-oriented mindset: integrate risk judgment from design through production, with focus on reliability, impact, and cost (not activity)
• Demonstrated experience using enterprise-authorized AI capabilities within the work environment to improve SRE workflows (e.g., incident investigation support and knowledge capture) with strong validation habits and awareness of data sensitivity.
• Ability to evaluate AI-assisted operational recommendations for correctness and risk, define appropriate guardrails for team usage, and ensure outcomes align to resiliency and security expectations.

Preferred qualifications, capabilities, and skills

• Networking depth (routing, switching, security, packet/flow analysis) or experience operating network-adjacent platforms: a strong plus, not a requirement
• Experience across multiple infrastructure domains or programming languages
• Demonstrated ongoing AI skill development (e.g. context/prompt engineering, agent orchestration) and use of AI to redesign workflows for measurable impact
• Prior experience in regulated or large-scale enterprise environments

• Experience establishing engineering culture