Software Engineer III (Java/Agentic)

JPMorganChase is a leading financial services firm providing a broad range of financial products and services. The company is seeking a Software Engineer III to design, develop, and deliver secure, scalable agentic system components and AI-driven solutions within its Auto Technology division. The role involves cloud deployment, collaboration across agile teams, application of agentic frameworks and GenAI, and ensuring code quality, security, and compliance.

Responsibilities:

• Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
• Develop modular components for agentic systems to enable intelligent automation and orchestration within business platforms; continuously build, test, and maintain these components
• Own individual workstreams and Jira stories, consistently delivering high-quality code and features as part of broader AI/ML initiatives
• Collaborate closely with team members to integrate agentic capabilities into existing systems and actively support solution delivery
• Containerize and deploy agentic system components on cloud platforms (preferably AWS), strictly following established DevOps and infrastructure-as-code practices
• Apply the latest advancements in agentic frameworks (such as LangGraph, Google ADK, AutoGen), LLMs, and GenAI to assigned tasks and features by staying up to date with industry developments
• Document design decisions, implementation details, and results; clearly communicate progress and technical concepts to team members and stakeholders
• Ensure code quality, security, and compliance by conducting thorough testing, peer reviews, and adhering to best practices
• 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