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AI Lead Software Engineer - Java
lead · Technology / Software Development
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.
Job responsibilities
- 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.
Required qualifications, capabilities and skills
- 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
Preferred qualifications, capabilities and skills
- 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