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 Enterprise technology, Infrastructure Platforns 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 Build reliability into the platform through production-grade software (automation, control loops, self-healing, and tooling) that eliminates manual operations instead of formalizing them. Use declarative, intent-based systems: store desired state as data in a trusted source of truth and continuously reconcile actual state to it via automation (not one-off imperative scripts). Treat telemetry and state data as core reliability assets: instrument consistently, collect at scale, and use the data to drive detection, diagnosis, and closed-loop remediation. Define SLIs/SLOs with stakeholders and operationalize them via SLO-based alerting, standard telemetry/observability practices, and alerts that are actionable and tied to user impact. Own services end-to-end—reliability, performance, security, and cost—lead on-call and major incidents (triage, mitigation, comms, blameless PIRs), drive measurable toil reduction, and apply AI across the SDLC with strict validation so speed never compromises correctness. 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. 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. Required Qualifications, Capabilities, and Skills Formal training or certification on site reliability engineering concepts and 5+ years applied experience 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. Strong production software engineering experience in an industry-standard language (Python, Go, Java, C++, Rust), delivering and maintaining real-world systems end to end. Proven ownership of production systems at scale, including on-call responsibility, incident response, postmortems, and designing for reliability/operability. Demonstrated SLI/SLO/error-budget practice (or clear ability and motivation to own and institutionalize it) and to drive reliability decisions with data. Deep observability skills: white-box + black-box monitoring, SLO-based alerting, and high-quality telemetry using platforms like Grafana/Prometheus, Splunk, Datadog, Dynatrace, or equivalents. Strong *nix fundamentals and infrastructure automation experience (e.g., Kubernetes, Terraform, CI/CD), with an “automate-to-scale” mindset. Systems thinking at scale: clear interfaces/contracts, understanding of failure modes, safe rollouts, and managing complex interactions across services and dependencies. Security-first, high-judgment operator and communicator: uses AI tools effectively for real engineering work, knows when not to, stays calm in high-severity events, and focuses on outcomes (reliability, impact, cost). 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