As a Sr Lead Site Reliability Engineer at JPMorgan Chase within theConsumer & Community Banking Data and Analytics team,you will help build a meaningful engineering discipline, combining software and systems to develop creative engineering solutions to operations problems. Much of our support and software development focuses on optimizing existing systems, building infrastructure and reducing work through automation. You27ll join a team of curious problem solvers with a diverse set of perspectives who are thinking big and taking risks. In this environment you27ll take the lead on relevant projects, supported by an organization that provides the support and mentorship you need to learn and grow. A

Job 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 every day and champions the adoption of site reliability 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
• Identify application patterns and analytics in support of better service level objectives
• Design self-healing and resiliency patterns
• Design 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 debug critical components of applications and platforms

• Uses enterprise-authorized AI capabilities within the work environment 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/auditability, resiliency, and security controls.

Required qualifications, capabilities, and skills

• Formal training or certification on site reliability engineering concepts and 5+ years 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 2D including reliability patterns, capacity planning, incident management, performance tuning, and toil reduction 2D 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 within the work environment 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 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, capabilities, and skills

• 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 ability to mentor and educate others on site reliability principles and practices.
• Recognized as an active contributor of the engineering community.