Sr Lead Software Engineer

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As a Senior Lead Software Engineer at JPMorgan Chase within the Corporate Technology Data Strategy & Architecture organization, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.

Job responsibilities

• Develops secure, high-quality production code for data-intensive applications and platforms, and reviews and debugs code written by others

• Leads end-to-end design and implementation of complex software features, from requirements through deployment and operational stability

• Drives technical decisions that influence application design, functionality, performance, and reliability

• Builds and maintains agentic AI systems, including multi-agent workflows, tool-use integrations, and human-in-the-loop controls appropriate for regulated financial services environments

• Implements LLM-based applications including RAG pipelines, embedding workflows, vector store integrations, and model serving infrastructure

• Owns observability, evaluation, and safety of production AI systems — including prompt monitoring, output validation, cost tracking, and latency optimization

• Identifies and executes opportunities to automate remediation of recurring issues and improve operational stability

• Executes creative software solutions, including design, development, and technical troubleshooting to solve complex and ambiguous problems

• Mentors and coaches junior and mid-level engineers, conducting code reviews and sharing engineering best practices

• Contributes to firmwide frameworks, tools, and SDLC practices as an engaged member of the engineering community

• Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.

• Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.