As a Lead Software Engineer - ML Engineer for Agent Platform at JPMorgan Chase within the Commercial and Investment Banking – Data Analytics Payments Team, you are an integral part of an agile team that builds and delivers NEO, the firm's agent runtime platform for Payments Technology. You lead hands-on engineering of major runtime components — secure execution, agent-to-agent communication, memory, retrieval, and evaluation — in a secure, stable, and scalable way. 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 major NEO runtime components — agent execution and sandboxing (micro-VMs), A2A and MCP integrations, the memory layer (memory nodes), retrieval, and evaluation harnesses • 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 applicaitons and systems • Implements permission-aware, auditable execution for agents, including fine-grained authorization and runtime policy checks • Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture • 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 • 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 • Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security • 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) • Experience with data observability, quality, and metadata management tools