There’s nothing more exciting than being at the center of a rapidly growing field in technology and applying your skillsets to drive innovation and modernize the world's most complex and mission-critical systems.
As a Site Reliability Engineer III at JPMorgan Chase within the Chief Data & Analytics Office (CDAO) AI/ML & Data Platforms team,, you will solve complex and broad business problems with simple and straightforward solutions. Through code and cloud infrastructure, you will configure, maintain, monitor, and optimize applications and their associated infrastructure to independently decompose and iteratively improve on existing solutions. You are a significant contributor to your team by sharing your knowledge of end-to-end operations, availability, reliability, and scalability of your application or platform.
Job Responsibilities
Guides and assists others in the areas of building appropriate level designs and gaining consensus from peers where appropriate, supporting adoption of site reliability engineering best practices within your team, including modern technologies such as Databricks, Snowflake, AWS, and Kubernetes
Collaborates with other software engineers and teams to design, develop, test, and implement deployment and reliability approaches using automated continuous integration and continuous delivery pipelines, while supporting application development and production environments
Uses enterprise-authorized AI capabilities within the work environment to accelerate incident triage, troubleshooting, and post-incident analysis, validating outputs and handling operational data according to sensitivity and security requirements, including developing and supporting AI/ML solutions for incident resolution
Implements infrastructure, configuration, and network as code for the applications and platforms in your remit
Collaborates with technical experts, key stakeholders, and team members to resolve complex problems, coordinate incident management coverage, and proactively address issues using service level indicators and objectives before they impact customers
Applies enterprise-authorized AI capabilities within the work environment to identify patterns in operational signals that indicate reliability risk or recurring toil, prioritizing reuse-first improvements tied to SLO outcomes, and supporting intelligent automation for troubleshooting
Familiar with availability, reliability, scalability, and solutions in their applications and works with partners to improve these outcomes iteratively, including participation in root cause analysis and implementing production changes
Proactively recognizes road blocks and identifies improvements to solve business problems, including exploring new technologies where appropriate
Required qualifications, capabilities, and skills
Formal training or certification on site reliability engineering concepts and 3+ years applied experience (NAMR/APAC – India/ LATAM/ Hong Kong)
Proficient in site reliability culture and principles and familiarity with how to implement site reliability within an application or platform, including strong understanding of SLI/SLO/SLA and error budgets
Proficient in at least one programming language such as Python, Java/Spring Boot, and .Net, including Python or PySpark for AI/ML modeling and automation to reduce operational toil by building tools for repeated tasks
Working knowledge of using enterprise-authorized AI capabilities within the work environment to support SRE workflows with strong validation habits and awareness of data sensitivity, including developing AI/ML solutions for troubleshooting and incident resolution
Ability to validate AI-assisted operational recommendations before applying changes, escalating when uncertain and following data sensitivity requirements, while ensuring compliance with risk controls and company-wide standards
Proficient knowledge of software applications and technical processes within a given technical discipline (e.g., Cloud, AI, Android, etc.), including hands-on experience in system design, resiliency, testing, operational stability, and disaster recovery
Experience in observability such as white and black box monitoring, service level objective alerting, and telemetry collection using tools such as Grafana, Dynatrace, Prometheus, Datadog, Splunk, and others
Experience with continuous integration and continuous delivery tooling, along with experience running production incident calls and managing incident resolution in collaboration with cross-functional teams
Familiarity with container and container orchestration and troubleshooting common networking technologies and issues, along with ability to work collaboratively in teams and build meaningful relationships to achieve common goals
Preferred qualifications, capabilities, and skills
- 4+ years in an SRE or production support role with AWS Cloud, Databricks, Snowflake or similar Technologies.
- AWS and Databricks certifications.