Join a team pioneering AI-first engineering to accelerate delivery and career growth. Leverage advanced tools and collaborate with experts to shape the future of application integration.
As a Software Engineer III at JPMorgan Chase within the Horizontal Engineering team, you will help build innovative, reusable engineering solutions for the AIS product line. You will work closely with senior engineers and AI agents to accelerate learning, delivery, and documentation. You will contribute to shared services, automation, and cross-product accelerators that improve delivery velocity and consistency. You will be part of a collaborative, inclusive team focused on advancing engineering practices and supporting career development.
Job responsibilities
- Design, develop, test, debug, and maintain secure, high-quality production code for reusable capabilities, automation, documentation, and cross-product components.
- Execute software solutions, design, development, and technical troubleshooting to build solutions and break down technical problems.
- Build shared modules, APIs, and engineering patterns for adoption across AIS products.
- Collaborate with senior engineers through pairing, shadowing, and guided AI-agent workflows to accelerate learning and improve delivery quality.
- 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.
- Create and maintain clear documentation, implementation notes, runbooks, and knowledge artifacts to support onboarding and repeatability.
- Analyze code, data, and delivery metrics to identify problems, improve coding hygiene, and strengthen system architecture.
- Produce architecture and design artifacts for complex applications, ensuring design constraints are met.
- Apply disciplined engineering practices including secure coding, CI/CD, automated testing, observability, resiliency, controls awareness, and timely escalation.
- Contribute to software engineering communities of practice focused on AI-first engineering, cloud-native delivery, controls automation, platform engineering, and developer productivity.
Required qualifications, capabilities and skills
- Formal training or certification on software engineering concepts and 3+ years applied experience
- Proficiency in Java and Python for application development and maintenance in a corporate or enterprise environment
- Working knowledge of the Software Development Life Cycle, agile delivery, CI/CD, secure coding, testing, resiliency, and production support practices
- Experience with React for building modern, responsive front-end applications
- Demonstrated knowledge of technical processes in areas such as cloud, AI, machine learning, platform engineering, controls engineering, automation, or infrastructure software
- Strong analytical ability, learning agility, collaboration skills, and ability to use AI-assisted development tools responsibly with appropriate validation, documentation, peer review, and escalation
- 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 APIs, infrastructure-as-code, CI/CD, observability, monitoring, or reusable platform components
- Exposure to cloud technologies across private cloud, AWS, Azure, or GCP
- Experience with controls automation, evidence generation, delivery analytics, or compliance tooling
- Strong interest in AI-first engineering practices, including generative AI, AI agents, automated testing, documentation generation, anomaly detection, or developer productivity tools
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