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, Web Hosting team , you hold a leadership role in your team, demonstrate strong knowledge across multiple technical domains, and advise others on the technical and business issue 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
- Demonstrates and champions site reliability culture and practices and exerts technical influence throughout your team
- Leads initiatives to improve the reliability and stability of web Hosting platforms using data-driven analytics to improve service levels
- Collaborates with team members to identify comprehensive service level indicators and stakeholders to establish reasonable service level objectives and error budgets with customers
- Demonstrates a high level of technical expertise within one or more technical domains and proactively identifies and solves technology-related bottlenecks in your areas of expertise
- 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.
- Collaborates with technical experts, key stakeholders, and team members to resolve complex problems
- Provides comprehensive and ongoing guidance, tools, and solutions to support the firms’ growth
- Works toward becoming an expert on the applications and platforms under your influence while understanding their interdependencies and limitations
- Documents and shares knowledge within your organization via internal forums and communities of practice
- 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
- Deep proficiency in reliability, scalability, performance, security, enterprise system architecture, toil reduction, and other site reliability best practices with the ability to implement these practices within an application or platform
- Advanced knowledge in site reliability culture and principles with demonstrated ability to implement site reliability within an application or platform
- 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.
- Advanced knowledge and experience in observability, monitoring, alerting, and telemetry collection using tools such as Grafana, Dynatrace, Prometheus, Cloudwatch, Splunk, etc.
- 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 knowledge and hands-on experience in SDLC process and software development
- Fluency in one programming language like Python, Ansible etc
- Strong communication skills with ability to mentor and educate others on site reliability principles and practices
- Deep knowledge of software applications and technical processes with emerging depth in one or more technical disciplines
Preferred qualifications, capabilities, and skills
- AWS/Azure Exposure (Understanding and working experience in AWS/Azure applications, and understanding of resiliency, scalability, observability, monitoring etc,)
- Experience as SRE in complex and mission critical applications involving multitude of components of varying technical generations
- Exposure in AI/ML
- Drive to self-educate and evaluate new technology