Senior Lead Site Reliability Engineer

About the role

Join a team of exceptionally gifted professionals and position yourself among the top echelon in site reliability. As a Sr Lead Site Reliability Engineer within the Consumer & Community Banking Data and Analytics team at JPMorgan Chase, you will help build a meaningful engineering discipline, combining software and systems to develop creative engineering solutions to operations problems. Much of your work will focus on optimizing existing systems, building infrastructure, and reducing work through automation.

Responsibilities

• Creates high-quality designs, roadmaps, and program charters that are delivered by you or the engineers under your guidance.
• Provides advice and mentoring to other engineers and acts as a key resource for technologists seeking advice on technical and business-related issues.
• Demonstrates site reliability principles and practices daily and champions their adoption throughout your team.
• Collaborates with others to create and implement observability and reliability designs for complex systems that are robust, stable, and do not incur additional toil or technical debt.
• Identifies application patterns and analytics to support better service level objectives.
• Designs self-healing and resiliency patterns.
• Designs automated software and product upgrades, change management, and release management solutions.
• Works toward becoming an expert on the applications and platforms in your remit while understanding their interdependencies and limitations.
• Evolves and debugs critical components of applications and platforms.
• Uses enterprise-authorized AI capabilities to accelerate reliability design and operational decisioning (e.g., incident/post-incident analysis and requirements traceability), validating outputs and handling operational data according to sensitivity and security requirements.
• Leads reuse-first adoption of AI-assisted reliability workflows across SDLC/toolchain practices (e.g., testing/validation automation and production readiness), ensuring traceability, resiliency, and security controls.

Requirements

• Formal training or certification on site reliability engineering concepts and 5+ years of applied experience.
• Advanced knowledge in site reliability culture and principles with demonstrated ability to implement site reliability within an application or platform.
• At least 2+ years of hands-on experience in architecting, scaling, and providing SRE support for AI/ML platforms and products, including infrastructure tech stacks such as Databricks, GPU clusters, Model Serving frameworks, Feature Stores, Vector Databases, and LLM inference pipelines.
• Demonstrated ability to apply core SRE fundamentals — including reliability patterns, capacity planning, incident management, performance tuning, and toil reduction — specifically to AI/ML and data-intensive, compute-heavy workloads.
• Experience in defining and enforcing SLOs/SLIs tailored to AI/ML workloads (e.g., model latency, throughput, data freshness, inference availability) to drive reliability at scale.
• Demonstrated experience using enterprise-authorized AI capabilities to improve reliability engineering workflows with strong validation habits and awareness of data sensitivity.
• Ability to set team practices for safe AI usage in operations (e.g., review/approval expectations and escalation paths) while maintaining resiliency, security, and auditability outcomes.
• Proven hands-on experience in designing and implementing Agentic AI-based solutions to deliver SRE capabilities at scale, including practical expertise with AI Agents, Skills, Context Management, Retrieval-Augmented Generation (RAG), and tool-use patterns.
• Ability to apply Agentic AI frameworks to automate and augment core SRE functions such as intelligent incident detection and remediation, automated root cause analysis, predictive alerting, self-healing infrastructure, runbook automation, and observability enrichment to reduce toil and accelerate MTTR.
• Contribute to governance and controls of AI usage with a site reliability mindset and principles of CCB systems and platforms.
• Advanced knowledge and experience in observability such as white and black box monitoring, service level objectives, alerting, and telemetry collection using tools such as Grafana, Dynatrace, Prometheus, Datadog, Splunk, etc.

Preferred Qualifications

• Experience with cloud-based data and analytics architecture, including AWS storage, Snowflake, Kubernetes (EKS), event-driven architectures, streaming services, batch jobs, and ETL pipelines.
• Proficiency with modern data processing frameworks such as Apache Kafka, Apache Spark, and similar tools, with a focus on ensuring scalability, reliability, and performance of data and analytics platforms.
• Strong communication skills with the ability to mentor and educate others on site reliability principles and practices.
• Recognized as an active contributor to the engineering community.

Benefits

We offer a competitive total rewards package including base salary determined based on the role, experience, skill set, and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching, and more. Additional details about total compensation and benefits will be provided during the hiring process.