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