Senior Lead Site Reliability Engineer

JPMorgan Chase & Co.; Buenos Aires; Presencial; ARS 3.000.000 - 6.000.000; Jornada completa

Descripción de la vacante

JPMorgan Chase & Co. is seeking a Senior Lead Site Reliability Engineer to define NFRs and availability targets for services within the IPFS team. You will ensure NFRs are design and test-embedded, measure customer experience with SLI/SLO, and define production-ready objectives with stakeholders.

You will lead observability and reliability designs for complex systems, mentor technologists, and drive AI-assisted reliability workflows across the SDLC while maintaining resiliency and security norms.

Formación

• Formal training or certification in site reliability engineering concepts with 5+ years hands-on experience.
• Advanced observability knowledge including monitoring, SLOs, alerting, and telemetry collection using tools like Grafana, Dynatrace, Prometheus, Datadog, Splunk.
• Expert-level proficiency in Java, Go, Python, and Terraform for enterprise-grade apps, high-performance systems, automation, and IaC.
• Experience applying enterprise AI capabilities to improve reliability workflows with strong validation and data-sensitivity awareness.
• Experience building AI agents and autonomous systems with frameworks like LangChain, LangGraph, AutoGen, CrewAI and logging pipelines (Fluentd, Logstash, Vector).
• Strong background in RESTful APIs, event-driven architectures with Kafka/RabbitMQ/SQS, and graph/time-series databases for AI-powered systems.
• Proficiency with containers (Docker/Kubernetes), CI/CD, and GitOps.
• Ability to communicate data-based solutions with complex reporting, and engage with vendors to assess fit with firm strategy.

Responsabilidades

• Creates and delivers high-quality designs, roadmaps, and program charters with the engineering team.
• Mentors technologists and promotes site reliability culture across the team.
• Collaborates on observability and reliability designs for robust systems with low toil and debt.
• Uses enterprise AI capabilities to accelerate reliability design and operational decisioning while ensuring security and auditability.
• Drives evolution and debugging of critical components by understanding interdependencies and limitations.
• Provides guidance, tools, and solutions to support JPMorgan Chase’s growth in reliability.
• Contributes to the site reliability community via internal forums and guilds.
• Leads AI-assisted reliability workflows across SDLC/toolchain with traceability and security controls.

Conocimientos

Site reliability engineering

Observability

Java

Go

Python

Terraform

AI in operations

Logging pipelines

RESTful APIs

Kubernetes

Message queues

Herramientas

Grafana

Dynatrace

Prometheus

Datadog

Splunk

LangChain

Descripción del empleo

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.

As a Senior Lead Site Reliability Engineer at JPMorgan Chase within the Infrastructure Platforms and Foundational Services (IPFS) team, you 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.

Job responsibilities

• Creates and delivers high quality designs, roadmaps, and program charters alongside the engineering team
• Acts as a key resource and mentor for technologists in your area seeking advice on technical and business issues, and serves as a culture carrier and site reliability adoption champion for your team
• Collaborates with others to create and implement observability and reliability designs for complex systems which are robust, stable, and do not incur additional toil or technical debt
• 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.
• Drives evolution and debugging of critical components by understanding application and platform interdependencies and limitations
• Provides comprehensive and ongoing guidance, tools, and solutions to support the firms’ growth
• Make significant contributions to JPMorganChase’s site reliability community via internal forums, communities of practice, guilds, and conferences
• 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
• 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
• 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
  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 and 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; and expertise in 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 qualifications, capabilities, and skills

• 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