Senior Lead Site Reliability Engineer
On-site · Palo Alto, California, United States
Job Summary
Senior Lead Site Reliability Engineer to design AI-powered automation, define non-functional requirements and availability targets for services within JPMorgan Chase's IPFS, ensure SLOs, implement autonomous AI-driven reliability solutions, mentor engineers, and contribute to the SRE community. Responsibilities include creating high-quality designs for AI-powered automation systems and observability solutions, building and maintaining AI Agents and MCP Servers, developing automation scripts and infrastructure-as-code with Java, Go, Python, and Terraform, designing logging pipelines and telemetry, and delivering RESTful APIs and event-driven architectures while collaborating with stakeholders across the organization.
Required Qualifications
- Formal training or certification in software engineering concepts with 5+ 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
- PreferredQualificationsIncludesBachelor/Master's in CS 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
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