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 JPMorganChase 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 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)

Preferred qualifications, capabilities, and skills:

- 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