As a Lead Software Engineer — AI, you will build and ship production-grade, multi-agent AI systems used daily by thousands of engineers. You’ll work on an applied AI platform that automates key stages of the software delivery pipeline—from PRD through architecture, implementation, QA, security, and retrospective—at enterprise scale.
You will own agent capabilities end-to-end, with a focus on reliability, evaluation, observability, and cost-per-session. This is not a research or data science role—you will engineer production systems that consistently follow specifications .
- As a Lead Software Engineer-AI with CCB at JPMorgan Chase you will develop complex and scalable frameworks using appropriate software design, including durable and reusable frameworks leveraged across teams and functions.
- Develops secure and high-quality production code, and reviews and debugs code written by others; engineers systems with clear failure modes, retry strategies, and robust operational behavior.
- Leads cross-functional teams on technological matters within domain of expertise while acting as the function’s go-to subject matter expert.
- Contributes to the development of technical methods in specialized fields in line with the latest product development methodologies, including pragmatic AI engineering practices.
- Builds and maintains evaluation and quality mechanisms to detect regressions, measure specification/acceptance-criteria compliance, and surface behavior changes across model or dependency updates.
- Influences leaders and senior stakeholders across business, product, and technology teams.
- Formal training or certification on software engineering concepts and 7+ years applied experience.
- Hands-on practical experience delivering system design, application development, testing, and operational stability.
- Proficient in one or more programming language(s) (e.g., Python and/or TypeScript; able to build CLI tooling, API integrations, and data pipelines).
- Experience applying expertise and new methods to determine solutions for complex technology problems in one or more technical disciplines (including designing multi-step AI workflows: sequential chains, parallel fan-out, conditional routing, and human-in-the-loop checkpoints).
- Knowledge of software application development and technical processes with considerable in-depth knowledge in one or more technical disciplines (e.g., cloud, artificial intelligence / applied LLM systems, machine learning, mobile, etc.).
- Experience with agent frameworks (e.g., LangChain, LlamaIndex, AutoGen, CrewAI, or homegrown) and clear opinions on their tradeoffs.
- Demonstrated experience building systems on top of LLM APIs (endpoint integration, streaming, tool/function calling, and context management).
- Ability to present and effectively communicate with Senior Leaders and Executives.
- Understanding of the business including familiarity with JPMC enterprise stack: Cloud Foundry, GKP, Jules CI/CD, TrueCD, Sophia auth.
- Practical cloud native experience.