Site Reliability Engineer III -(AIML SRE)

JPMorgan Chase • Jersey City, NJ

97d

About The Position

Are you looking for an exciting opportunity to join a dynamic and growing team in a fast paced and challenging area? This is a unique opportunity for you to work in our team to partner with the Business to provide a comprehensive view. As a Senior AI Reliability Engineer at JPMorgan Chase within the Technology and Operations division, you will join our dynamic team of innovators and technologists. Your mission will be to enhance the reliability and resilience of AI systems that revolutionize how the Bank services and advises clients. You will focus on ensuring the robustness and availability of AI models, deepening client engagements, and promoting process transformation. We seek team members passionate about leveraging advanced reliability engineering practices, AI observability, and incident response strategies to solve complex business challenges through high-quality, cloud-centric software delivery.

Requirements

• Formal training or certification on AI reliability concepts and 3+ years applied experience.
• Demonstrate a strong sense of curiosity and a passion for continuous learning, especially in the rapidly evolving field of AI reliability.
• Show proficiency in reliability, scalability, performance, security, enterprise system architecture, toil reduction, and other site reliability best practices.
• Possess deep knowledge and experience in observability, including white and black box monitoring, SLO alerting, and telemetry collection using tools such as Grafana, Dynatrace, Prometheus, Datadog, and Splunk.
• Be proficient with continuous integration and delivery tools like Jenkins, GitLab, or Terraform, as well as container and orchestration technologies such as ECS, Kubernetes, and Docker.
• Have experience troubleshooting common networking technologies and issues, and understand the unique challenges of operating AI infrastructure, including model serving, batch inference, and training pipelines.
• Communicate effectively and bridge the gap between ML engineers and infrastructure teams, with proven experience implementing and maintaining SLO/SLA frameworks for business-critical services, and working with both traditional and AI-specific metrics.

Nice To Haves

• 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.
• Understand ML model deployment strategies and their reliability implications.
• Have contributed to open-source infrastructure or ML tooling.
• Have experience with chaos engineering and systematic resilience testing.

Responsibilities

• Define and refine Service Level Objectives (SLOs) for large language model serving and training systems, using metrics like accuracy, fairness, latency, drift targets, TTFT, and TPOT, while balancing reliability and development velocity.
• Design, implement, and continuously improve monitoring systems to track availability, latency, drift, and other key metrics for robust observability and rapid issue detection.
• Collaborate in the design and deployment of high-availability language model serving infrastructure that supports high-traffic internal workloads across multiple regions and cloud providers.
• Champion site reliability engineering practices, providing technical leadership and fostering a culture of reliability, resilience, and continuous improvement across teams.
• Develop and manage automated failover and recovery systems for model serving deployments, ensuring seamless operation and rapid recovery from failures.
• Create and lead AI-specific incident response playbooks for issues like model drift or bias spikes, including automated rollbacks, circuit breakers, and systematic post-incident improvements.
• Build and maintain cost optimization systems for large-scale AI infrastructure, leveraging load balancing, caching, optimized GPU scheduling, and AI Gateways to ensure efficient, secure, and scalable operations.

What This Job Offers

• Career Level: Senior
• Industry: Credit Intermediation and Related Activities