Principal Software Engineer

JPMorgan Chase JPMorgan Chase · Banking · Palo Alto, CA +1 · Consumer & Community Banking

Principal Software Engineer at JPMorgan Chase focused on building and enhancing Java frameworks using Spring Boot, with a strong emphasis on integrating AI solutions, RAG, and applied AI into enterprise products. The role involves architectural design, technical leadership, and driving innovation with cutting-edge technologies in a secure and scalable manner.

What you'd actually do

  1. Design, develop, and maintain complex, scalable, and reusable Java frameworks using Spring Boot, ensuring they meet industry standards for reliability, efficiency, and performance.
  2. Lead the creation and adoption of coding patterns and best practices across the organization’s development community, driving standardization and consistency.
  3. Architect and implement robust, secure, and high-performance frameworks for both cloud and on-premises environments, leveraging cloud-native services (e.g., AWS).
  4. Collaborate with cross-functional teams to define integration strategies and technical solutions aligned with business goals.
  5. Provide technical thought leadership, staying abreast of industry trends, emerging technologies, and best practices to guide the team and organization.

Skills

Required

  • Java (Core Java & EE)
  • Spring Boot
  • Spring frameworks (Spring MVC, Spring Cloud, Spring GraphQL, Spring Security, Spring AI)
  • Microservices development
  • Cloud-native development (AWS)
  • API design and security
  • Relational database skills (SQL, data modeling)
  • Streaming technologies (Kafka, RabbitMQ)
  • CI/CD pipelines
  • DevOps practices
  • Unit and integration testing
  • Agile development processes (SCRUM/KANBAN)
  • AI Engineering
  • Retrieval-Augmented Generation (RAG)
  • Applied AI

Nice to have

  • AWS Lambda
  • AWS ECS
  • AWS S3
  • AWS Aurora
  • AWS API Gateway
  • JIRA
  • GitHub/Bitbucket
  • Jenkins
  • Maven/Artifactory

What the JD emphasized

  • Deep expertise in Java (Core Java & EE), Spring Boot, and related Spring frameworks (Spring MVC, Spring Cloud, Spring GraphQL, Spring Security, Spring AI).
  • Proven experience building performant, scalable, and reliable microservices and frameworks for both cloud (AWS) and on-premises deployments.
  • Advanced knowledge of API design, development, and security, with hands-on experience in enterprise-grade API solutions.
  • Proficiency in Relational database skills, including SQL, data modeling, and experience with high availability database architectures.
  • Advanced knowledge and hands-on experience with streaming technologies (Kafka, RabbitMQ, etc.) and s strong experience with CI/CD pipelines, cloud-native development (AWS Lambda, ECS, S3, Aurora, API Gateway), and DevOps practices.
  • Experience with unit and integration testing frameworks (JUnit, mocking frameworks, test-driven development).
  • Demonstrated ability to think strategically, develop and execute technical strategies, and drive organizational objectives. Ability to communicate effectively and present technical concepts to senior leaders and executives.
  • Proven track record of technical thought leadership, including identifying and addressing technical and process gaps, and elevating team capabilities using excellent analytical, problem-solving, and decision-making skills.
  • Demonstrated technical thought leadership in AI, guiding teams on best practices for AI integration, staying abreast of emerging trends, and driving the adoption of innovative technologies. Experience and exposure to AI Engineering, Retrieval-Augmented Generation (RAG), Applied AI, and integrating AI solutions into enterprise frameworks.
  • Experience with Agile development processes (SCRUM/KANBAN) and tools (JIRA, GitHub/Bitbucket, Jenkins, Maven/Artifactory).
  • Formal training or certification in software engineering concepts, with 12+ years of applied experience in system design, application development, testing, and operational stability.

Other signals

  • integrating AI solutions into enterprise frameworks
  • Retrieval-Augmented Generation (RAG)
  • Applied AI