As a Lead Site Reliability Engineer at JPMorganChase within the Corporate sector, Enterprise Technology team, you are an integral part of a team that develops high-quality architecture solutions for critical software applications and platforms. You will lead resiliency design reviews, break down complex problems, and mentor engineers, driving significant business impact and shaping the target state architecture through your expertise in multiple architecture domains. 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.

Job responsibilities

• Demonstrate and champion site reliability culture and practices, exerting technical influence across your team
• Lead initiatives to improve reliability and stability of applications and platforms using data-driven analytics
• Collaborate with team members to define service level indicators and work with stakeholders to establish service level objectives and error budgets
• Provide technical leadership and guidance for medium to large-sized products
• Proactively identify and resolve technology-related bottlenecks in your areas of expertise
• Act as the main point of contact during major incidents, quickly identifying and solving issues to avoid financial losses
• Document and share knowledge within the organization through internal forums and communities of practice
• Use 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
• Lead 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
• Offer mentorship and advice to other engineers, fostering a culture of continuous improvement
• Drive collaboration with stakeholder partners to establish reasonable service level objectives and error budgets

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• At least 5 years as an SRE and at least 10 years in a highly regulated industry such as Banking
• Deep proficiency in reliability, scalability, performance, security, enterprise system architecture, toil reduction, and site reliability best practices, with the ability to implement these practices within an application or platform
• Demonstrated experience designing, deploying, and supporting highly available services in a public cloud environment (AWS, Azure, or GCP); familiarity with cloud-native observability, auto-scaling, and infrastructure-as-code is essential
• Fluency in at least one programming language (e.g., Python, Java Spring Boot, .Net)
• Deep knowledge of software applications and technical processes with emerging depth in one or more technical disciplines
• Proficiency and experience in observability, including white and black box monitoring, SLO alerting, and telemetry collection using tools such as Grafana, Dynatrace, Prometheus, Datadog, Splunk
• Proficiency in continuous integration and continuous delivery tools (e.g., Jenkins, GitLab, Terraform)
• Experience with containers and container orchestration (e.g., ECS, Kubernetes, Docker)
• Experience troubleshooting common networking technologies and issues
• 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

• Ability to identify and solve problems related to complex data structures and algorithms
• Drive to self-educate and evaluate new technology
• Ability to teach new programming languages to team members
• Ability to expand and collaborate across different levels and stakeholder groups