Site Reliability Engineer III

Site Reliability Engineer III

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 Consumer & Community Banking Data and Analytics 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
• Collaborates with other software engineers and teams to design and implement deployment approaches using automated continuous integration and continuous delivery pipelines
• Collaborates with other software engineers and teams to design, develop, test, and implement availability, reliability, scalability, and solutions in their applications
• 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
• Understands service level indicators and utilizes service level objectives to proactively resolve issues before they impact customers
• Supports the adoption of site reliability engineering best practices within your team

• 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.

• 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.

Required Qualifications, Capabilities, and Skills

• Formal training or certification on site reliability engineering concepts and 3+ years applied experience
• Exposure to or hands-on experience in supporting SRE practices for AI/ML platforms and products, with familiarity in infrastructure components such as Databricks, Vector Databases, Model Serving endpoints, and ML training/deployment pipelines.
• Understanding of how to apply SRE fundamentals — including monitoring, incident response, capacity awareness, and toil identification — to AI/ML and data-intensive workloads, with the ability to define and track relevant SLOs/SLIs (e.g., model latency, inference availability, data freshness).
• Familiarity with Agentic AI concepts such as AI Agents, Skills, Context Management, and Retrieval-Augmented Generation (RAG), with the ability to leverage these tools to support SRE functions like incident triage, alert enrichment, runbook automation, and root cause analysis.
• Experience in observability including white and black box monitoring, service level objective alerting, and telemetry collection using tools such as Grafana, Dynatrace, Prometheus, Datadog, Splunk, etc.
• Experience with platforms and applications hosted on public/private/hybrid cloud environments, including container orchestration technologies such as Kubernetes, ECS, and Docker.
• Experience with continuous integration and continuous delivery tools such as Jenkins, GitLab, or Terraform.
• Familiarity with troubleshooting common networking technologies and issues.

• 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

• Ability to validate AI-assisted operational recommendations before applying changes, escalating when uncertain and following data sensitivity requirements

Preferred Qualifications, Capabilities, and Skills

• Experience supporting reliability practices for AI/ML platforms, including model serving endpoints and ML pipelines.
• Experience with Databricks, vector databases, or large-scale feature or embedding pipelines in production environments.
• Experience applying automation techniques to reduce toil, including self-healing workflows and runbook automation.
• Experience with Kubernetes and containerized workloads in production. Proficient in site reliability culture and principles with familiarity in implementing site reliability practices within an application or platform. Formal training or certification in SRE concepts is preferred.
• Familiarity with distributed tracing practices for complex, multi-service systems.
• Experience running chaos engineering or game day resilience exercises.
• Familiarity with agentic AI concepts (for example, retrieval-augmented generation) to assist incident triage and operational workflows