Lead Software Engineer

JPMorgan Chase JPMorgan Chase · Banking · Columbus, OH +1 · Consumer & Community Banking

Lead Software Engineer role focused on building secure, cloud-native services using Java, Spring Boot, AWS, and Kafka. The role emphasizes driving team adoption of enterprise-authorized AI-assisted engineering practices for code quality, delivery speed, and operational outcomes, with a strong focus on responsible AI use, security, and compliance within engineering workflows.

What you'd actually do

  1. 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.
  2. 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.
  3. Design, develop, test, deploy, and support secure, scalable, and resilient software solutions using Java, Spring Boot, AWS cloud technologies, and Kafka
  4. Build and maintain RESTful APIs, microservices, backend services, and event-driven integrations
  5. Develop secure, high-quality production code and review, debug, and improve code written by others

Skills

Required

  • Java
  • Spring Boot
  • AWS cloud technologies
  • Kafka
  • RESTful APIs
  • microservices
  • distributed backend services
  • Software Development Life Cycle
  • agile methodologies
  • CI/CD
  • Application Resiliency
  • Security
  • secure production code
  • code reviews
  • engineering standards
  • troubleshooting
  • problem-solving
  • production stability

Nice to have

  • modern middleware services technologies
  • containerized application delivery
  • Kubernetes-based platforms
  • Kafka-based event-driven architecture
  • messaging platforms
  • streaming technologies
  • infrastructure-as-code
  • cloud deployment automation
  • performance tuning
  • resiliency testing
  • monitoring
  • logging
  • alerting
  • highly controlled or regulated technology environments
  • security
  • identity
  • access
  • fraud
  • trust
  • risk
  • customer protection platforms

What the JD emphasized

  • 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