Lead Software Engineer - Agentic AI, Java/Python

Jersey City, NJ, United StatesSenior5+ years

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)

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