Software Engineer III (Full Stack)

JPMorgan Chase & Co.•Jersey City, NJ

2d•$137,750 - $185,000

About The Position

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. As a Software Engineer III at JPMorganChase within the Audit Technology, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Requirements

Formal training or certification on software engineering concepts and 3+ years applied experience
Demonstrated experience building distributed applications using Java 8+ with Spring Boot/Spring and RESTful microservices.
Demonstrated experience developing and maintaining services or automation in Python in a large enterprise environment.
Working knowledge of modern frontend development with React and JavaScript/TypeScript, including API integration patterns.
Experience building and operating software using CI/CD practices (automated builds, tests, quality checks, packaging, deployment, and environment promotion).
Experience with containerization and orchestration (Docker, ECS and/or Kubernetes) and deploying to cloud environments (AWS preferred).
Solid understanding of application resiliency and production readiness practices (logging/monitoring, alerting, performance, and operational support).
Solid understanding of secure software development practices (authentication/authorization patterns, secrets management concepts, and vulnerability remediation).
Strong problem-solving skills, ability to meet deadlines, and track record of delivering high-quality code.
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
Overall knowledge of the Software Development Life Cycle
Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security

Nice To Haves

Experience implementing or integrating LLM-based capabilities (e.g., retrieval patterns, evaluation approaches, guardrails) in enterprise applications.
Exposure to developing AI Agent-style solutions/workflows and related orchestration patterns.
Familiarity with additional frontend frameworks such as Angular.
Experience with cloud service integrations and event-driven/messaging patterns is a plus.
Strong research, investigation, presentation, and evaluation skills, with the ability to communicate technical tradeoffs clearly.

Responsibilities

Design, build, test, deploy, and maintain enterprise applications for Internal Audit in close collaboration with business and customer stakeholders.
Develop cloud-native microservices (Java and Python), including defining service contracts (REST APIs) and integrating with internal and third-party services.
Build and enhance rich client user experiences using React and modern web engineering practices.
Deploy services to container-based runtime environments and support releases to private/public cloud environments (AWS preferred).
Create proof of concepts with new frameworks and libraries, identify best practices, and adapt quickly to evolving requirements.
Perform design reviews and peer code reviews, and promote code quality standards (readability, testing, security, resiliency, maintainability).
Build and maintain CI/CD pipelines and automated release processes, including promoting application packages across environments.
Participate in Scrum ceremonies, technical reviews, and continuous improvement efforts for configuration management, deployment, and operational readiness.
Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
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.
Support operational stability by contributing to monitoring, incident triage, root-cause analysis, and preventative fixes.