Java Aws Lead Software Engineer

JPMorgan Chase JPMorgan Chase · Banking · New York, NY +1 · Consumer & Community Banking

Lead Software Engineer for Consumer and Community Banking Connected Commerce team, focusing on enhancing and delivering technology products. The role involves driving team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality and delivery speed, while also setting validation standards and promoting reuse. Responsibilities include writing secure Java/Spring Boot code, troubleshooting, gathering data, and leading communities of practice. Requires experience in system design, application development, and leading software engineers, with a strong understanding of responsible AI use and coaching engineers on safe adoption.

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. Sets up monitoring and reliability for both infrastructure and models (drift detection, model accuracy) using Prometheus/Grafana.
  4. Designs, develops, codes, and troubleshoots with consideration of upstream and downstream systems and technical implications.
  5. Gathers, analyzes, and draws conclusions from large, diverse data sets to identify problems and contribute to decision-making in service of secure, stable application development.

Skills

Required

  • Formal training or certification on software engineering concepts and 5+ years applied experience
  • Hands-on practical experience in system design, application development, testing, and operational stability.
  • 8+ years experience in full life cycle development and experience leading varying levels of Software Engineers
  • 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
  • Strong hands on coding in Java and Sprint Boot, with good understanding of Spring Cloud concepts and AWS
  • Hands-on experience to utilize monitoring and tracing tools (e.g. Splunk, Dynatrace, Postman, etc)
  • Demonstrated knowledge of software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence/machine learning, etc.)
  • Exposure to agile methodologies such as CI/CD, Application Resiliency, and Security
  • Participate in code reviews, troubleshooting, and performance tuning
  • Contribute to system architecture and technical decision-making
  • Leadership experience with varying levels of Software Engineers

Nice to have

  • Proven expertise in architecting and delivering highly scalable, resilient, and stable systems on public cloud platforms such as AWS
  • Extensive experience with event-driven messaging and streaming technologies (e.g., Kafka), both NoSQL and relational databases, and cloud-native API/microservice development
  • Strong hands-on proficiency with modern software development tools and technologies, including Jira, Confluence, IntelliJ IDEA, Maven, Git, Jenkins, Sonar, Artifactory, and AI-powered code assistants

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