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Based on 240 similar postings.
Employer
About JPMorgan Chase
JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.
JPMorgan Chase currently has
1117 open roles
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Listed pay typically runs
$186,160–$215,000
across 7 roles with salary data.
As a Senior Lead Software Engineer - ML Engineer for Agent Platform within the Commercial and Investment Banking – Data Analytics Payments Team, you will serve as a technical leader building and operating NEO, the firm's agent runtime platform for Payments Technology. You will own the end-to-end architecture of the runtime, including secure execution via micro-VMs like Firecracker or Kata, agent-to-agent communication, Model Context Protocol tooling, memory layers, and evaluation infrastructure. Your daily work involves developing high-quality production code, defining permission-aware execution models using OpenFGA and OPA/Rego, and leading communities of practice. You will utilize Python, Go, or Rust to build scalable systems in a regulated environment. Key technical components include Kubernetes, Terraform, Docker, LLM/agent systems with RAG architectures, vector databases, and AI-assisted engineering practices to solve complex problems in payment systems infrastructure.
Design the end-to-end architecture for the NEO agent runtime, including secure execution, communication protocols, and memory layers.
Develop high-quality production code in Python and systems languages like Go or Rust for performance-sensitive tasks.
Define permission-aware, auditable execution models using fine-grained authorization and policy engines to ensure safe operation in regulated environments.
Establish and enforce coding standards, design patterns, and validation protocols across all teams building on the platform.
Integrate AI-assisted engineering practices into the development lifecycle to improve code quality, testing, and incident analysis.
Automate the remediation of recurring issues to enhance the operational stability of the agent runtime and its hosted agents.
Lead technical evaluations with external vendors and internal teams to assess architectural designs and technology applicability.
Mentor senior engineers and lead communities of practice to promote the adoption of emerging technologies.
What we're looking for
Formal training or certification on software engineering concepts and 5+ years of applied experience.
Hands-on experience delivering system design, application development, testing, and operational stability for production platforms or distributed systems.
Advanced proficiency in Python and a systems language such as Go or Rust.
Experience building or operating LLM/agent systems in production, including tracing, evaluations, and guardrails.
Demonstrated experience leading the use of AI-assisted software development tools while ensuring security and performance standards.
Strong understanding of responsible AI practices, data sensitivity, and secure handling of inputs/outputs.
Knowledge of financial services industry IT systems and practical cloud-native experience with production Kubernetes.
Experience with micro-VMs, agent protocols (A2A, MCP), memory systems, RAG architectures, or Infrastructure as Code (preferred).
Python
LLMs
RAG
Kubernetes
Docker
Terraform
Micro-VMs
CI/CD
Machine Learning
Vector Databases
Agentic Systems
Infrastructure as Code
Agile
Software Development Life Cycle
Data Observability
Graph RAG
Firecracker
Kata
gVisor