Lead Software Engineer - Agentic AI, Java/Python

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 - Agentic AI, Java/Python at JPMorgan Chase within the Commercial and Investing Banking - Data Analytics Payments Team, you are an integral part of an agile team that delivers trusted technology products in a secure, stable, and scalable way. You will collaborate with senior stakeholders to design, drive growth of J.P. Morgan’s Data and Analytics solutions and enable data-driven sales growth strategies. You lead hands-on engineering of driving intelligence automation and build agentic AI on NEO, the firm's agent runtime platform for Payments Technology.. As a core technical contributor, you are responsible for delivering 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 the ability to think beyond routine or conventional approaches to build solutions or break down technical problems - Builds and operates multiple agentic solutions on NEO end to end — agent execution and sandboxing (micro-VMs), A2A and MCP integrations, the memory layer (memory nodes), retrieval, and evaluation harnesses - Delivers the full-stack product surface – Typescript/Next.js application on Salt Design System with virtualization at scale. - Develops secure and high-quality production code, and reviews and debugs code written by others - Drives team adoption of enterprise-authorized AI-assisted engineering practices 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 - Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of the software applications and systems - Implements permission-aware, auditable execution for agents, including fine-grained authorization and runtime policy checks - Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies, and mentors Lead and senior engineers - Adds to team culture of diversity, opportunity, inclusion, and respect Required qualifications, capabilities, and skills - Formal training or certification on software engineering concepts and 5+ years applied experience - Hands-on practical experience delivering system design, application development, testing, and operational stability - Advanced in one or more programming language(s); strong Python required - Experience delivering production React (Next.js) web applications, including real-time UI (SSE/WebSocket) and working within a component/design system - 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 - Hands-on experience building LLM-power or agentic systems, including tracing, evaluations, and guardrails - Proficient in all aspects of the Software Development Life Cycle - Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning) - In-depth knowledge of the financial services industry and their IT systems - Practical cloud native experience; production Kubernetes expected Preferred qualifications, capabilities, and skills - Exposure to LLMs, RAG architectures, vector databases, and embedding-based retrieval systems; Graph RAG a plus - Experience with agent protocols (A2A, MCP) or multi-agent orchestration - Experience with sandboxed/secure code execution (containers and micro-VMs such as Firecracker, Kata, gVisor) - Experience with agent memory (memory nodes, episodic/semantic memory) or graph-backed retrieval - Familiarity with building or running evals for LLM/agent systems - Proficiency with Infrastructure as Code (Terraform) and containerized deployments (Docker, Kubernetes)