Sr Lead Software Engineer - Java

JPMorgan Chase JPMorgan Chase · Banking · Palo Alto, CA +1 · Commercial & Investment Bank

Senior Lead Software Engineer role focused on enhancing and building technology products within JPMorgan Chase's Commercial & Investment Bank. The role involves providing technical guidance, developing secure code, and driving the adoption of AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes. It requires strong Java backend experience, cloud-native AWS knowledge, and experience with event-driven architectures. A key aspect is leading the effective use of enterprise-authorized AI tools and ensuring responsible AI usage in engineering workflows.

What you'd actually do

  1. Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
  2. Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
  3. Develops secure and high-quality production code, and reviews and debugs code written by others
  4. Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
  5. Drives decisions that influence the product design, application functionality, and technical operations and processes

Skills

Required

  • Formal training or certification on software engineering concepts and 5+ years applied experience
  • Hands on experience in back-end software engineering ideally with Java as the primary programming language, delivering production-grade services at scale.
  • Hands-on practical experience delivering system design, application development, testing, and operational stability for distributed back-end platforms.
  • Advanced in Java and common back-end frameworks and patterns (e.g., Spring Boot, REST/gRPC, microservices), with strong understanding of performance and concurrency.
  • Advanced knowledge of event-driven architectures and messaging/streaming systems (e.g., Kafka, AWS SNS/SQS, or comparable platforms), including event design, ordering, retries, and idempotency.
  • Ability to tackle design and functionality problems independently with little to no oversight, producing clear technical direction and measurable outcomes.
  • Practical cloud native experience on AWS, including building, deploying, and operating services using managed cloud capabilities and infrastructure-as-code.
  • Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.
  • Familiarity with applying AI capabilities to build solutions and enhance engineering workflows, including responsible use of AI-assisted development tools and patterns.

Nice to have

  • Experience with AI-powered development tools such as GitHub Copilot, Claude Code, or similar coding assistants to accelerate development workflows

What the JD emphasized

  • AI-assisted engineering practices
  • AI-assisted code review/refactoring
  • measurable validation standards
  • approved AI-assisted development and automation capabilities
  • enterprise-authorized AI-assisted software development tools
  • validating AI outputs for correctness, performance, and security
  • responsible AI use in engineering workflows
  • data sensitivity considerations
  • secure handling of inputs/outputs
  • resiliency and security expectations
  • compliant usage patterns and controls
  • applying AI capabilities to build solutions
  • responsible use of AI-assisted development tools and patterns