AI-Native Full Stack Software Engineer III - JAVA

JPMorganChase is at the forefront of applying AI to transform how software is built, operated, and delivered at scale. If you are an engineer who sees AI as a force multiplier — not just a tool — this is your opportunity to shape the next generation of enterprise platforms and intelligent workflow solutions at one of the world's most influential technology organizations.

As an AI-Native Full Stack Software Engineer III at JPMorganChase within the Enterprise Technology organization, you will design, build, and operate modern cloud-native applications and AI-powered solutions that drive measurable improvements in developer productivity and business outcomes. You will work at the intersection of full-stack engineering and advanced AI development — building copilots, agents, and orchestration frameworks that redefine how teams deliver software. This role offers the opportunity to grow your expertise across the full AI-native engineering lifecycle while collaborating with talented engineers across the firm.

Job responsibilities

• Design and develop scalable full-stack applications using modern front-end, back-end, API, and cloud-native technologies
• Build AI-enabled applications, copilots, and intelligent agents that automate workflows and improve engineering and business outcomes
• Develop and integrate retrieval-augmented generation (RAG) pipelines, knowledge bases, and multi-agent orchestration frameworks into production systems
• Leverage AI-assisted development tools to accelerate software delivery, testing, documentation, and operational support across the development lifecycle
• Implement secure, resilient, and observable cloud-native solutions aligned with engineering best practices
• Create reusable AI skills, tools, plugins, and evaluation frameworks that can be adopted across engineering teams
• Contribute to the adoption of AI-native engineering practices, helping elevate the team's collective capability and delivery velocity
• Collaborate cross-functionally with product, architecture, and platform teams to translate business requirements into scalable technical solutions
• Support production operations through monitoring, troubleshooting, and continuous improvement of deployed applications and AI systems

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 3+ years applied experience
• Proficiency in Java with hands-on experience building back-end services and RESTful APIs using Spring Boot
• Demonstrated experience developing front-end applications using React and modern JavaScript or TypeScript
• Experience designing and building APIs, microservices, and distributed systems in cloud-native environments
• Solid understanding of CI/CD pipelines, automated testing, DevOps practices, observability, and production operations
• Hands-on experience with AI platforms and ecosystems such as OpenAI, Anthropic, Microsoft Copilot, Google Gemini, or AWS AI Services
• Experience building AI applications using frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, or equivalent agent orchestration tools
• Working knowledge of prompt engineering, context engineering, agent collaboration patterns, and AI-assisted software development workflows
• Understanding of multi-agent architectures, Model Context Protocol (MCP), agentic RAG, tool integration, and workflow orchestration concepts
• Experience with memory management, context management, workflow state tracking, and agent observability in production AI systems

Preferred qualifications, capabilities, and skills

• Experience with Amazon Bedrock, Bedrock Agents, AgentCore, or SageMaker for building and deploying AI-powered solutions
• Familiarity with knowledge base implementation, guardrails, model evaluation, and AI governance patterns
• Demonstrated track record of using AI tooling — such as GitHub Copilot, Claude Code, Cursor, or similar — to measurably improve code quality, testing, security, or delivery speed
• Experience contributing to or leading adoption of AI-native engineering practices within a team or organization