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 JPMorgan Chase within the Asset & Wealth Management, 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.
Job responsibilities
- Drives large-scale, cross-functional initiatives from concept through delivery, architecting cloud-native solutions on AWS while remaining deeply engaged in system design, code-level decisions, and engineering best practices
- Mentors junior engineers, foster a culture of ownership and innovation, and partner closely with business and executive stakeholders to align technology investments with organizational priorities.
- Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
- Provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
- Develops secure and high-quality production code, and reviews and debugs code written by others. Drives decisions that influence the product design, application functionality, and technical operations and processes
- Serves as a function-wide subject matter expert in one or more areas of focus. Actively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle.
- 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
- Expert-level proficiency in Java,React, AWS, and system design, with a proven ability to architect scalable, distributed solutions while leading cross-functional teams
- Expertise in the Java/JVM ecosystem — including Spring Boot, microservices, and event-driven architectures — will be central to shaping the platform's technical direction
- Experience in driving enterprise-wide technology programs across multiple teams, managing complexity and delivering measurable outcomes.
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Advanced knowledge of software applications and technical processes with considerable in-depth knowledge in one or more technical disciplines (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
- Ability to tackle design and functionality problems independently with little to no oversight and Practical cloud native experience
- 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
- Experience with Python, AI/ML, and databases is a plus.
- Beyond the core requirements in Java, AWS, and system design, we value additional depth in Python, AI/ML (including LLMs and generative AI), and relational and NoSQL databases.
- Experience in financial services or regulated industries are highly valued.