Lead Software Engineer - Agentic AI

JPMorgan Chase & Co.•Plano, TX

20h

About The Position

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 JPMorganChase within the Consumer and Community Banking - Deposits 2.0 platform, 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.

Requirements

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 TypeScript / 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, WebSockets, 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., LangGraph, AutoGen, CrewAI, LlamaIndex Workflows, Semantic Kernel).
Tool Calling & Function Calling: Deep expertise in structuring model tool calls, JSON schema validation, dynamic API integration, sandboxed code execution, and MCP (Model Context Protocol).
Architecture & Memory Management: Implementing short-term and episodic memory (scratchpads, state graphs, vector-based retrieval, conversational buffer compaction).
LLM Foundations: Advanced prompt engineering, chain-of-thought, ReAct (Reasoning + Acting), reflection loops, and output grounding/guardrails (e.g., NeMo Guardrails, Guardrails AI)

Nice To Haves

Experience building agent-based systems, orchestration patterns, or agent development tooling and evaluation frameworks
Experience designing scalable inference or model serving architectures, including latency, throughput, and cost optimization
Familiarity with responsible artificial intelligence practices, model risk concepts, and governance-by-design approaches
Experience contributing to or maintaining widely used open-source software in machine learning or infrastructure ecosystems
Domain knowledge applying machine learning to regulated financial services use cases

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.