Lead Site Reliability Engineer

Job Description

As a Lead Site Reliability Engineer at JPMorgan Chase within the AI/ML & Data Platform 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 Demonstrates and champions site reliability culture and practices and exerts technical influence throughout your team Leads initiatives to improve the reliability and stability of your team s applications and 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 Acts as the main point of contact during major incidents for your application and demonstrates the skills to identify and solve issues quickly to avoid financial losses Documents and shares knowledge within your organization via internal forums and communities of practice Required qualifications, capabilities, and skills Formal training or certification on SRE concepts and 5+ years applied experience Expertise in SRE principles, reliability, scalability, and performance of application and infrastructure. Expertise in programming with Python and Infrastructure as Code, tools such as Terraform. Experience in architecting distributed systems and cloud-native architecture in AWS. Systematic problem-solving and troubleshooting skills in a complex system. Excellent communication skills and ability to represent and present business and technical concepts to stakeholders. Preferred qualifications, capabilities, and skills Preferred experience working in AI, ML, or Data engineering. Familiar in container orchestration/Kubernetes. Familiar in developing Automation frameworks/AI Ops.