About the job
Elevate your engineering prowess to unprecedented levels by joining a team of exceptionally gifted professionals and position yourself among the top echelon in site reliability and AI-powered infrastructure automation. As a Senior Lead Site Reliability Engineer at JPMorgan Chase within the Infrastructure Platforms and Foundational Services (IPFS) organization, you will work with your fellow stakeholders to define non-functional requirements (NFRs) and availability targets for the services in your application and product lines. You will ensure those NFRs are accounted for in your products' design and test phases, that your service level indicators are effectively measuring customer experience, and that service level objectives are defined with stakeholders and implemented in production. This role uniquely combines traditional SRE excellence with cutting-edge AI/ML capabilities to build autonomous systems that revolutionize how we operate infrastructure at enterprise scale.
Responsibilities
Creates high quality designs, roadmaps, and program charters for AI-powered automation systems, intelligent monitoring solutions, and next-generation reliability platforms 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, particularly in the intersection of SRE and AI/ML technologies
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, including comprehensive logging pipelines and systems that export, analyze, and visualize observability metrics and traces across distributed systems
Designs and builds AI Agents and MCP (Model Context Protocol) Servers for autonomous operations including incident detection, root cause analysis, and auto-remediation, while architecting solutions that integrate multiple data stores including graph databases, vector databases, transactional databases, analytical databases, and big data platforms
Develops automation scripts and infrastructure-as-code using Java, Go, Python, and Terraform to improve operational efficiency, and builds and maintains RESTful services, APIs, and message queue architectures for event-driven systems and platform automation
Makes significant contributions to JPMorgan Chase's site reliability community via internal forums, communities of practice, guilds, and conferences
Qualifications
Minimum
Formal training or certification in software engineering concepts with 10+ years of applied experience in Site Reliability Engineering, DevOps, or Software Engineering
Advanced knowledge in site reliability culture and principles with demonstrated ability to implement site reliability within an application or platform
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.
Expert-level proficiency in Java, Go (Golang), Python, and Terraform for building enterprise-grade applications, high-performance systems, automation, and infrastructure as code
Advanced knowledge of software applications and technical processes with considerable depth in multiple technical disciplines including distributed systems, microservices architecture, and cloud-native technologies
Hands-on experience building AI Agents and autonomous systems with proficiency in AI frameworks (LangChain, LangGraph, AutoGen, CrewAI) and leveraging AI development tools (GitHub Copilot, Claude, etc.) to accelerate development and innovation
Expertise in designing and implementing logging pipelines (Fluentd, Logstash, Vector) and systems for metrics collection, analysis, and distributed tracing
Strong experience building production-grade RESTful APIs and designing message queue architectures (Kafka, RabbitMQ, SQS) for event-driven systems
Experience with graph databases (Neo4j, TigerGraph), vector databases (Pinecone, Weaviate, Chroma), and integrating multiple data stores for AI-powered systems
Proficiency with containerization (Docker, Kubernetes), CI/CD pipelines, and GitOps workflows
Ability to communicate data-based solutions with complex reporting and visualization methods, recognized as an active contributor of the engineering community, and continues to expand network and leads evaluation sessions with vendors to see how offerings can fit into the firm's strategy
Preferred
Bachelor's or Master's degree in Computer Science, Engineering, or related field
Experience with MCP (Model Context Protocol) Servers or similar agent frameworks for building autonomous systems, and understanding of LLM integration, prompt engineering, and RAG (Retrieval-Augmented Generation)
Familiarity with AI/ML model building, deployment, and lifecycle management using frameworks like TensorFlow, PyTorch, or scikit-learn
Experience with big data technologies (Hadoop, Spark, Flink), analytical databases, NoSQL databases (MongoDB, Cassandra, DynamoDB), and time-series databases (InfluxDB, TimescaleDB)
Knowledge of security best practices and compliance requirements in highly regulated industries, with experience in chaos engineering tools (Chaos Monkey, Gremlin, LitmusChaos) and GameDay exercises
Contributions to open-source projects, particularly in SRE, observability, or AI/ML domains, and certifications in cloud platforms (AWS, Azure, GCP)
Strong communication skills with ability to mentor and educate others on site reliability principles and practices, and ability to anticipate, identify, and troubleshoot defects found during testing