Lead Software Engineer - Agentic AI

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.  

As a Lead Software Engineer at JPMorgan

Chase within the Consumer and Community Banking - Deposits 2.0 team, 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. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems

Define and drive the platform roadmap for agent-based capabilities, focusing on measurable outcomes, reliability, and usability

Lead end-to-end delivery of core agent platform components, including software development kits, reference implementations, and integration patterns

Partner with product, engineering, risk, and control stakeholders to align requirements, prioritize trade-offs, and unblock execution

Establish quality, performance, and operational standards for agent workloads, including monitoring, testing, and incident readiness

Translate experimentation into production by driving clear architecture decisions, scalable designs, and repeatable deployment practices

Guide responsible development practices by embedding governance, privacy, and model risk considerations into platform design

Communicate technical strategy and progress to senior stakeholders with clarity, data, and pragmatic recommendations

Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.

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

Formal training or certification on software engineering concepts and 5+ years applied experience Proficiency in Python (primary for agent orchestration and LLM tooling) and/or Type Script / Java / Go for enterprise backend integration.​Data & RAG Systems:
Designing hybrid search pipelines (dense vector retrieval, BM25, rerankers) paired with vector databases like Pinecone, Milvus, Qdrant, or pgvector.​Backend & API Design:
Building scalable microservices using FastAPI, Spring Boot, or Node.js to expose agent interfaces (REST, Web Sockets, Server-Sent Events for streaming tokens and tool calls).Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.

Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices

Agentic Frameworks & Orchestration:
Production experience with multi-agent and workflow orchestration frameworks (e.g., Lang Graph, Auto Gen,…