Lead Software Engineer - ML Engineer for Agent Platform

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 - 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