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AWS Lead Software Engineer-Full Stack/Java/SpringBoot

Job Description - AWS Lead Software Engineer-Full Stack/Java/SpringBoot

Description

Be an integral part of an agile team that’s constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As an AWS Lead Software Engineer-Full Stack/Java/SpringBoot at JPMorgan Chase within Commercial and Investment Banking's Payments Technology team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading healthcare technology product in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

 

Job responsibilities

  • Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
  • Develops secure high-quality production code, and reviews and debugs code written by others
  • Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
  • Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
  • Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
  • 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
  • Adds to team culture of diversity, opportunity, inclusion, and respect

     

Required qualifications, capabilities, and skills

  • Formal training or certification on Software Engineering concepts and 5+ years applied experience
  • Hands-on experience coding in Java, Spring Boot, JPA, SQL, REST
  • Experience with event driven architecture using Kafka
  • Experience building RESTful backend applications using Spring Boot and solid relational enterprise databases with Oracle
  • Experience with version control systems, such as Git.
  • Strong knowledge of CI/CD e.g. Jenkins
  • Hands-on practical experience in system design, application development, testing and operational stability
  • Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
  • 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
  • Solid understanding of cross cutting concerns such as Application Resiliency, and Security

     

Preferred qualifications, capabilities, and skills

  • Familiarity with Groovy and Gradle
  • Experience coding in Python
  • Strong understanding of containerization technologies such as Kubernetes or Docker
  • Exposure to AI/ML technologies – especially familiarity with model training and inference
  • Exposure to AWS cloud technologies


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