Lead Site Reliability Engineer

As a Lead Site Reliability Engineer at JPMorgan Chase in Infrastructure Platforms, you are a code-first software engineer specializing in reliability. You will design and build the automation, tooling, and observability that keep enterprise-scale platforms resilient, and you own those systems end-to-end, including SLIs/SLOs, incident leadership, and toil reduction. You will write software that makes enterprise-scale infrastructure reliable, observable, and self-healing. You will work AI-native, using AI fluently across the software development lifecycle to build faster, respond to incidents quicker, and engineer away manual operations, while retaining full accountability for correctness, security, reliability, and cost.

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

Required Qualifications, Capabilities, and Skills Solid production coding experience in an industry-standard language (e.g. Python, Go, Java, C++, Rust): this is a software engineering role

Experience running production systems at scale, including on-call ownership, incident response, and designing for reliability and operability

Experience with SLI/SLO/error-budget practice, or clear aptitude and appetite to own it

Observability depth: white-box/black-box monitoring, SLO-based alerting, and telemetry, using tools such as Grafana, Prometheus, Splunk, Datadog, Dynatrace, or equivalent

Experience with *nix and with infrastructure automation and tooling (e.g. Kubernetes, Terraform, CI/CD)

Strong systems thinking: interfaces, contracts, failure modes, and interactions at scale

Fluency directing AI tools to do real engineering work, not just autocomplete, with sound judgment on where AI applies and where deep human expertise is required

Security-first mindset, integrating risk judgment from design through production

Clear, direct communication and calm ownership under pressure during high-severity events

Outcome orientation: focused on reliability, impact, and cost, not activity

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