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.

Job Responsibilities:
 

Develop 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

Design, implement and continuously improve monitoring systems including availability, latency and other salient metrics

Collaborate in the design and implementation of high-availability language model serving infrastructure capable of handling the needs of high-traffic internal workloads

Champion site reliability culture and practices, providing technical leadership and influence across teams to foster a culture of reliability and resilience

Champion site reliability culture and practices and exerts technical influence throughout your team

Develop and manage automated failover and recovery systems for model serving deployments across multiple regions and cloud providers

Develop AI Incident Response playbooks for AI-specific failures like sudden drift or bias spikes, including automated rollbacks and AI circuit breakers.
Lead incident response for critical AI services, ensuring rapid recovery and systematic improvements from each incident
Build and maintain cost optimization systems for large-scale AI infrastructure, ensuring efficient resource utilization without compromising performance.

Engineer for Scale and Security, leveraging techniques like load balancing, caching, optimized GPU scheduling, and AI Gateways for managing traffic and security.

Collaborate with ML engineers to ensure seamless integration and operation of AI infrastructure, bridging the gap between development and operations.

Implement Continuous Evaluation, including pre-deployment, pre-release, and continuous post-deployment monitoring for drift and degradation.

 

Required qualifications, capabilities, and skills:

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

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
Have excellent communication skills
 

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.

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