Senior Lead Site Reliability Engineer

JPMorganChase•Palo Alto, CA

18d

About The Position

As a Senior Lead Site Reliability Engineer on JPMorgan Chase’s Infrastructure Platforms and Foundational Services (IPFS) team, you will collaborate with stakeholders to define non-functional requirements and availability targets for services across application and product lines. You will ensure these requirements are incorporated into product design and testing, that service level indicators measure customer experience effectively, and that service level objectives are defined with stakeholders and implemented in production. The role involves advancing reliability engineering, observability, and AI-assisted operational practices across complex systems. You will guide engineers, promote reuse-first reliability workflows, and contribute to the firm’s site reliability community. The position is subject to Section 19 of the Federal Deposit Insurance Act; an employment offer is contingent on JPMorgan Chase’s review of criminal conviction history, including pretrial diversions or program entries.

Requirements

Formal training or certification in site reliability engineering concepts and at least 5 years of applied experience.
Advanced knowledge of site reliability culture and principles, with demonstrated ability to implement site reliability within an application or platform.
Advanced observability knowledge and experience, including white- and black-box monitoring, service level objectives, alerting, and telemetry collection; tools may include Grafana, Dynatrace, Prometheus, Datadog, and Splunk.
Expert-level proficiency in Java, Go, Python, and Terraform for enterprise-grade applications, high-performance systems, automation, and infrastructure as code.
Demonstrated experience using enterprise-authorized AI capabilities to improve reliability engineering workflows, with strong validation habits and awareness of data sensitivity.
Ability to establish safe AI operational practices while maintaining resiliency, security, and auditability.
Advanced knowledge of software applications and technical processes, with depth in multiple technical disciplines including distributed systems, microservices, and cloud-native technologies.
Hands-on experience building AI agents and autonomous systems using frameworks such as LangChain, LangGraph, AutoGen, or CrewAI, and AI development tools such as GitHub Copilot or Claude.
Expertise in designing logging pipelines using tools such as Fluentd, Logstash, or Vector, and systems for metrics collection, analysis, and distributed tracing.
Strong experience building production-grade RESTful APIs and designing message queue architectures such as Kafka, RabbitMQ, or SQS for event-driven systems.
Expertise in graph databases such as Neo4j or TigerGraph, vector databases such as Pinecone, Weaviate, or Chroma, and integrating multiple data stores for AI-powered systems.
Proficiency with Docker, Kubernetes, CI/CD pipelines, and GitOps workflows.
Ability to communicate data-based solutions through complex reporting and visualization methods and to contribute to the engineering community.
Ability to expand professional networks and lead vendor evaluation sessions to assess how offerings fit the firm’s strategy.

Nice To Haves

Hands-on experience operating on-premises data-center infrastructure, such as enterprise networking, storage, data-center services, or data protection and replication, and applying site reliability practices to physical or hybrid infrastructure.
Experience with MCP (Model Context Protocol) Servers or similar agent frameworks, and understanding of LLM integration, prompt engineering, and RAG.
Familiarity with AI/ML model building, deployment, and lifecycle management using TensorFlow, PyTorch, or scikit-learn.
Experience with big data technologies such as Hadoop, Spark, or Flink; analytical databases; NoSQL databases such as MongoDB, Cassandra, or DynamoDB; and time-series databases such as InfluxDB or TimescaleDB.
Knowledge of security best practices and compliance requirements in highly regulated industries, and experience with chaos engineering tools such as Chaos Monkey, Gremlin, or LitmusChaos and GameDay exercises.
Contributions to open-source projects, particularly in SRE, observability, or AI/ML, and cloud platform certifications in AWS, Azure, or GCP.
Strong communication skills, ability to mentor and educate others on site reliability principles and practices, and ability to anticipate, identify, and troubleshoot defects found during testing.

Responsibilities

Create and deliver high-quality designs, roadmaps, and program charters alongside the engineering team.
Serve as a technical resource and mentor, advise technologists on technical and business issues, and act as a culture carrier and site reliability adoption champion.
Collaborate on observability and reliability designs for complex systems that are robust and stable and avoid additional toil or technical debt.
Use enterprise-authorized AI capabilities to accelerate reliability design and operational decision-making, validate outputs, and handle operational data according to sensitivity and security requirements.
Drive evolution and debugging of critical components by understanding application and platform interdependencies and limitations.
Provide ongoing guidance, tools, and solutions to support the firm’s growth.
Contribute to JPMorgan Chase’s site reliability community through internal forums, communities of practice, guilds, and conferences.
Lead reuse-first adoption of AI-assisted reliability workflows across SDLC and toolchain practices, including testing and validation automation and production readiness, while ensuring traceability, auditability, resiliency, and security controls.
Set team practices for safe AI usage in operations, including review and approval expectations and escalation paths.
Communicate data-based solutions using complex reporting and visualization methods, contribute to the engineering community, expand professional networks, and lead vendor evaluation sessions.