The role
From JPMorgan Chase's own posting.
As a Lead Software Engineer at JPMorganChase, you will be at the center of a high-impact modernization effort, leading the evolution of firmwide alerting and event-routing infrastructure from a legacy monolith to a modern, cloud-native microservices platform. You will partner across engineering, site reliability, product, and controls teams to deliver resilient, scalable systems — including a new AI-powered monitoring tool that brings intelligent event enrichment, correlation, and root cause analysis to the firm. This is a brilliant role for an engineer with a full stack (but backend-dominant) Java skillset; who brings deep technical expertise to raise the bar for the teams around them.
Job Responsibilities
Execute creative software solutions across design, development, and advanced technical troubleshooting, think beyond routine approaches to build solutions and break down complex technical problems.
Lead the migration from a legacy monolith to a modern, containerized microservices architecture (Java/Spring Boot; UI modernization as needed), including decomposition strategy, domain modeling, and data migration planning.
Re‑architect existing infrastructure to achieve high scalability, reliability, and availability (multi‑AZ/region patterns, autoscaling, HA/DR).
Deliver features end‑to‑end, build backend services and APIs (REST/GraphQL) in Java/Spring Boot and contribute to operational UIs/console experiences as needed.
Design and implement event‑driven systems and streaming‑based alerting workflows (e.g., Kafka), including sound topic/schema design and resilient consumer strategies.
Build and evolve secure, high‑quality production services; review, debug, and improve code written by others to raise engineering standards.
Drive performance, resiliency, and scalability improvements for firmwide alerting and event routing; instrument SLOs/SLIs and optimize p99 latency and throughput. Collaborate cross‑LOB with product, application teams, SRE/operations, and controls to ensure reliable adoption and effective incident response.
Explore and evaluate AI for Ops approaches with partners across SRE and observability. Drive POCs and data‑driven adoption where appropriate.
Lead evaluation and architecture discussions with internal teams (and vendors when applicable) to assess designs, technical credentials, and fit within enterprise architectures. Contribute to communities of practice to promote awareness and adoption of modern engineering and observability practices.
Add to a team culture of diversity, opportunity, inclusion, and respect.
Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Required qualifications, capabilities, and skills
Significant professional software engineering experience, including leading cross‑team initiatives (Lead level).
Hands‑on experience delivering system design, application development, testing, and operational stability for distributed systems.
Advanced proficiency in Java and building microservices with Spring Boot, strong system design expertise.
Backend-focused engineering mindset with the ability to contribute across layers as needed, including partnering effectively on UI work when required.
Proficiency in automation and continuous delivery methods; experience implementing CI/CD pipelines.
Proficient across the Software Development Life Cycle (SDLC): requirements, design, coding, testing, deployment, and support.
Advanced understanding of agile methodologies and practices, including CI/CD, application resiliency, and security.
Cloud‑native experience deploying containerized microservices in cloud.
Working knowledge of relational databases (Oracle).
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Preferred qualifications, capabilities, and skills
Proficiency with Kubernetes (e.g., EKS) for container orchestration and operations.
Proficiency with event‑streaming platforms (e.g., Kafka) and event‑driven architectural patterns.
Experience designing and operating distributed systems at scale (microservices, multi‑region failover).
Experience building modern frontend applications with React and TypeScript for operational consoles/UX (good to have).
Expertise with AWS services aligned to containerized workloads and streaming. Familiarity with CockroachDB is a plus.
Infrastructure‑as‑Code (e.g., Terraform, Helm) and configuration management. Observability tooling familiarity (e.g., Prometheus/Grafana, OpenTelemetry, Splunk/ELK) and defining SLOs/SLIs.
Experience mentoring engineers, conducting design reviews, and establishing engineering standards/best practices. Effective communication and stakeholder management across product, operations, and controls.