Site Reliability Engineer AIML Associate
Site Reliability Engineer AIML Associate
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Job Description
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
Required Experience:
IC
Employment Type
Full-Time
