Job Description

JOB DESCRIPTION

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 Asset & Wealth Management, 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

Develops and refine Service Level Objectives( including metrics like accuracy, fairness, latency, drift targets, TTFT (Time To First Token), and TPOT (Time Per Output Token)) for large language model serving and training systems, balancing availability/latency with development velocity Designs, implement and continuously improve monitoring systems including availability, latency and other salient metrics Collaborates in the design and implementation of high-availability language model serving infrastructure capable of handling the needs of high-traffic internal workloads Champions site reliability culture and practices, providing technical leadership and influence across teams to foster a culture of reliability and resilience Develops and manage automated failover and recovery systems for model serving deployments across multiple regions and cloud providers Develops AI Incident Response playbooks for AI-specific failures like sudden drift or bias spikes, including automated rollbacks and AI circuit breakers. Leads incident response for critical AI services, ensuring rapid recovery and systematic improvements from each incident Builds and maintain cost optimization systems for large-scale AI infrastructure, ensuring efficient resource utilization without compromising performance. Engineers for Scale and Security, leveraging techniques like load balancing, caching, optimized GPU scheduling, and AI Gateways for managing traffic and security. Collaborates with ML engineers to ensure seamless integration and operation of AI infrastructure, bridging the gap between development and operations. Implements Continuous Evaluation, including pre-deployment, pre-release, and continuous post-deployment monitoring for drift and degradation.

Required qualifications, capabilities, and skills

Formal training or certification on software engineering concepts and 3+ years applied experience Demonstrated proficiency in reliability, scalability, performance, security, enterprise system architecture, toil reduction, and other site reliability best practices Proficient knowledge and 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 Proficient with continuous integration and continuous delivery tools like Jenkins, GitLab, or Terraform. Proficient with container and container orchestration: (ECS, Kubernetes, Docker) Experience with troubleshooting common networking technologies and issues Understand the unique challenges of operating AI infrastructure, including model serving, batch inference, and training pipelines Have proven experience implementing and maintaining SLO/SLA frameworks for business-critical services Are comfortable working with both traditional metrics (latency, availability) and AI-specific metrics (model performance, training convergence) Can effectively bridge the gap between ML engineers and infrastructure teams

Preferred qualifications, capabilities, and skills

Experience with AI-specific observability tools and platforms, such as OpenTelemetry and OpenInference. Familiarity with AI incident response strategies, including automated rollbacks and AI circuit breakers. Knowledge of AI-centric SLOs/SLAs, including metrics like accuracy, fairness, drift targets, TTFT (Time To First Token), and TPOT (Time Per Output Token). Expertise in engineering for scale and security, including load balancing, caching, optimized GPU scheduling, and AI Gateways. Experience with continuous evaluation processes, including pre-deployment, pre-release, and post-deployment monitoring for drift and degradation.

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