Job Description

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

As a Software Engineer III - Java Full Stack Developer + Python + AWS at JPMorgan Chase within the Commercial & Investment Bank, you'll 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.

Job responsibilities

Design, develop, and maintain scalable and robust applications using Java, Spring Boot and React.
Collaborate with cross-functional teams to define, design, and ship new features.
Implement cloud-native solutions on AWS, ensuring high availability, scalability, security and Engage in prompt engineering for LLMs and write Python scripts.
Utilize AI tech tools such as GitHub Copilot and MCP servers to enhance productivity and code quality.
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.
Participate in code reviews, providing constructive feedback to peers.
Troubleshoot and resolve complex technical issues across the application stack.
Stay up-to-date with emerging technologies and industry trends, and apply them to improve existing systems and processes.
Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
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.

Required qualifications, capabilities, and skills

Formal training or certification on software engineering concepts and 3+ years applied experience
Proven experience in software development using Core Java, REST, Spring Boot, Spring modules, Hibernate, Junit and Mockito.
Experience in UI technologies like jQuery, HTML, React and Strong skills around design principles and design patterns with the ability to troubleshoot and debug an application for any application issues.
Strong understanding of cloud platforms - AWS and experience deploying applications in a cloud environment.
Experience with CI/CD pipelines, containerization (Docker, Kubernetes), version control systems (Git) and messaging and integration frameworks like Kafka.
Experience in writing SQL queries, database management concepts, JDBC, and persistence API frameworks like Spring JPA.
Experience in AI/ML with focus on Generative AI, AI Agents Framework like Lang chain and Lang graph, Python, LLMs, RAG, Prompt engineering.
Excellent problem-solving skills and the ability to work independently and collaboratively in a team environment.
Strong communication skills, both written and verbal, with the ability to articulate complex technical concepts to non-technical stakeholders and Ability to multi-task and also work independently in order to meet the committed deadlines and Knowledge in Spec Driven development architecture.
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.

Preferred qualifications, capabilities, and skills