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 an AI ML Lead Site Reliability Engineer at JPMorgan Chase within the AIML 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 teamLeads initiatives to improve the reliability and stability of your team’s applications and platforms using data-driven analytics to improve service levelsCollaborates with team members to identify comprehensive service level indicators and stakeholders to establish reasonable service level objectives and error budgets with customersDemonstrates a high level of technical expertise within one or more technical domains and proactively identifies and solves technology-related bottlenecks in your areas of expertiseActs 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 lossesPartner with product engineering teams to ensure the AI/ML systems are reliable and high performing.Develop observability, security, automation and fin-ops tools and orchestration.Provide strategic technology leadership by defining and evaluating standards and architecture for reliability, observability and automation frameworks.Build strong cross-functional relationships that foster engagements across the organization and deliver solutions to user problems.Debug and solve issues in a production environment, identify root cause and remediate.Participates in on-call rotations, incident management and escalation workflows.

Required qualifications, capabilities, and skills

Formal training or certification on site reliability engineering concepts and proficient 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 platformFluency in at least one programming language such as (e.g., Python, Java Spring Boot, .Net, etc.)Deep knowledge of software applications and technical processes with emerging depth in one or more technical disciplinesProficiency and experience in observability such as white and black box monitoring, SLO alerting, and telemetry collection using tools such as Grafana, Dynatrace, Prometheus, Datadog, Splunk, etc.Proficiency in continuous integration and continuous delivery tools (e.g., Jenkins, GitLab, Terraform, etc.)Experience with container and container orchestration (e.g., ECS, Kubernetes, Docker, etc.)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.Self-managed, self-motivated with strong sense of ownership, urgency, and drive
 Preferred qualifications, capabilities, and skillsPrior experience working in AI, ML, or Data engineering.Expertise in container orchestration/Kubernetes.Prior experience developing Automation frameworks/AI OpsPrior experience building observability and telemetry tools.