As a Lead Software Engineer - ML Engineer for Agent Platform at JPMorgan Chase within the Commercial and Investment Banking 6 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 6 secure execution, agent-to-agent communication, memory, retrieval, and evaluation 6 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 6 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