Neural Interface Engineer Jobs in Illinois (NOW HIRING)

Join one of the world's most innovative financial institutions and be part of a team that is redefining how artificial intelligence and full stack engineering power financial services for millions of customers. At JPMorganChase, we invest in our engineers, offering you the tools, technology, and talent network to grow your career while solving some of the most complex and high-impact challenges in the industry.

As a Software Engineer III at JPMorganChase within the Commercial & Investment Bank, you will design, build, and operate real-time AI agents and full stack applications that run in live production environments and enable critical business capabilities across the firm. You will bring hands-on production experience engineering intelligent, autonomous systems using modern AI frameworks, Java-based backend APIs, and React-driven front-end interfaces. Your contributions will directly shape how the firm leverages AI and engineering innovation to serve millions of customers every day.

Our team thrives on real-world problem solving, critical thinking, and a shared commitment to engineering excellence. You will work in a fully onsite environment in Plano, TX, where bold ideas are welcomed, continuous learning is encouraged, and your hands-on production experience makes a measurable difference across the organization.

Job responsibilities

• Design, build, and operate real-time AI agents in live production environments, applying hands-on experience with agent frameworks, neural cognitive task systems, and autonomous decision-making pipelines
• Engineer scalable backend services and Java APIs that power AI-driven workflows, ensuring high availability, low latency, and production-grade reliability across enterprise systems
• Develop and maintain responsive, user-facing front-end interfaces using React, integrating seamlessly with backend AI services and Java APIs to deliver cohesive full stack experiences
• Implement and maintain continuous integration workflows, automating build, test, and deployment pipelines to ensure rapid, reliable delivery of production software
• Execute software solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions or break down complex technical problems
• Create secure and high-quality production code and maintain algorithms that run synchronously with appropriate systems, applying critical thinking to diagnose and resolve complex, real-world engineering challenges
• Leverage 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; contribute learnings and reusable patterns to improve broader team effectiveness
• Apply 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
• Proactively identify hidden problems and patterns in data and use these insights to drive improvements to coding hygiene, AI agent performance, and system architecture
• Participate in code reviews, applying structured problem-solving and critical thinking to provide constructive feedback that continuously elevates engineering standards across the team

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 3+ years applied experience
• Hands-on practical experience in system design, application development, testing, and operational stability
• Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
• Demonstrated hands-on production experience building, deploying, and operating real-time AI agents in live environments - academic or project-only experience will not be considered
• Hands-on production experience with AI agent frameworks, neural cognitive task systems, and AI skillset orchestration in enterprise-scale backend engineering contexts
• Proficiency in Java for backend API development with demonstrated experience building and maintaining scalable, production-grade services and integrations
• Hands-on experience with React for building dynamic, responsive, and user-facing front-end applications integrated with backend AI and API services
• Demonstrated experience implementing and maintaining continuous integration pipelines using industry-standard tooling to support automated build, test, and deployment workflows
• 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

Preferred qualifications, capabilities, and skills

• Experience with cloud-based platforms and services such as AWS, Azure, or Google Cloud to support scalable AI agent deployment and backend infrastructure
• Familiarity with containerization and orchestration tools such as Kubernetes and Docker for managing production AI workloads
• Exposure to event-driven architectures and real-time data streaming technologies such as Kafka to support high-throughput AI agent pipelines
• Experience working within financial services or highly regulated enterprise environments with a strong understanding of data security, compliance, and responsible AI requirements
• Knowledge of distributed systems design patterns, microservices architecture, and API development best practices in the context of AI-integrated systems