AI Lead Software Engineer - Java

We have an exciting opportunity to advance your career and drive meaningful impact by pushing the limits of modern engineering.

As a Lead Software Engineer at JPMorganChase within Corporate Technology, you will be a key technical leader on an agile team designing and delivering secure, scalable, high-performing technology solutions. You will partner across product, business, and engineering stakeholders to build strategic AI platforms that enable the firm's objectives.

- Lead the design, development, and delivery of high-quality software solutions that solve complex business problems with modern engineering practices - Drive architectural decisions and technical direction to improve scalability, resiliency, and availability for distributed systems - Build and maintain secure, production-grade services, setting a high bar for code quality through reviews, testing, and engineering excellence - Mentor engineers through technical guidance, pairing, and actionable feedback to raise team capability and delivery outcomes - Implement continuous integration and continuous delivery automation and reliability patterns to support safe, frequent releases - Develop data-intensive services and workflows using Java or Scala, working effectively with large-scale datasets and modern data platforms - Design and build AI-enabled applications, including retrieval-augmented generation patterns and agentic workflows, with appropriate evaluation and guardrails - 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.

- Formal training or certification on software engineering concepts and 5+ years applied experience - Demonstrated hands-on experience designing and delivering scalable, secure, resilient distributed systems in a production environment - Proficiency in Java and Spring Boot, with working knowledge of SQL and modern user interface frameworks (for example, React) - Strong understanding of modern architecture patterns, including microservices, event-driven design, and application programming interface-first approaches - Experience building and operating solutions on public cloud platforms (for example, Amazon Web Services), including containerization and orchestration (Docker and Kubernetes) - Experience implementing continuous integration and continuous delivery pipelines and Infrastructure as Code practices to improve release safety and speed - Practical experience with application programming interface design and integration patterns (for example, REST and GraphQL), including security and performance considerations - 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

- Advanced expertise in object-oriented design, system design, and performance optimization for large-scale services - Experience with modern data and search technologies (for example, Elasticsearch) and databases (for example, Oracle or MongoDB) - Experience building generative AI solutions, including retrieval-augmented generation architecture, orchestration frameworks (for example, LangChain or LlamaIndex), and evaluation practices - Experience creating reusable agent skills, libraries, or patterns that accelerate delivery across engineering teams - Experience working across hybrid technology ecosystems using cloud services, Databricks, and Kubernetes-based platforms