As an Lead Software Engineer at JPMorganChase within Enterprise Technology, you will lead the architecture and hands-on implementation of scalable large language model systems and agentic AI platforms for enterprise use cases leveraging LLM Suite. You will design cloud-native solutions, establish evaluation and observability standards, and drive technical decisions across teams to improve reliability, cost, and developer velocity. LLM Suite is JPMorganChase's premier internally built AI tool leveraged by +250k employees for everything from individual productivity to larger scale, business solutions.
Build and scale production AI platforms that turn large language model capabilities into reliable, secure, and measurable business outcomes. You will partner across product and engineering teams to design architectures, ship reusable capabilities, and raise quality through strong engineering practices and technical leadership.
Job responsibilities
- Lead the architecture and hands-on delivery of scalable, reliable agentic AI platforms for enterprise workflows
- Design and build production-grade AI systems including agents, skills, memory patterns, guardrails, and tool-use orchestration
- Architect retrieval and context-engineering approaches including embeddings, semantic search, grounding, summarization, and prompt/version management
- Engineer cloud-native AI services on AWS using containers and serverless patterns, event-driven messaging, and distributed data stores
- Optimize platform performance across latency, throughput, scalability, caching, context efficiency, and cost controls
- Build well-governed APIs and integrations that connect AI capabilities to enterprise platforms, tools, and business processes
- Establish evaluation, experimentation, regression testing, and observability frameworks to continuously improve quality and agent behavior
- Define engineering standards for reliability, security, and safe AI operation across the platform lifecycle
- Mentor senior engineers and influence engineering direction through code reviews, architecture forums, and cross-team technical leadership
- 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.
Required qualifications, capabilities and skills