JP Morgan Chase

Runs a consumer bank and an investment bank

Lead Software Engineer - Multiple Teams

AI Application Engineering +1Confirmed live in the last 24 hours

Full-timeNo salary listedIn-person

About the Job

Requirements

Formal training or certification in software engineering concepts and at least 5 years of applied experience.
Hands-on experience delivering system design, application development, testing, and operational stability for production systems.
Advanced proficiency in Python and/or Java, with a strong preference for both.
Working knowledge of React and modern frontend fundamentals to contribute to or lead full-stack feature delivery.
Demonstrated end-to-end full-stack engineering across services and APIs, integrations, continuous integration and continuous delivery automation, and production operations.
Practical, hands-on AWS experience building cloud-native services across compute, storage, networking, identity and access management, and observability.
Proficiency across all aspects of the Software Development Life Cycle.
Advanced understanding of agile methodologies, continuous integration and continuous delivery, application resiliency, and security-by-design.
Demonstrated experience leading effective use of approved AI-assisted software development tools for coding, code review, test acceleration, and troubleshooting, including setting validation expectations for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows, including data sensitivity, secure handling of inputs and outputs, and resiliency and security expectations; experience coaching engineers on safe, compliant adoption.
Experience mentoring engineers and leading code and design reviews in an enterprise environment, such as financial services.

Responsibilities

Design, build, and maintain an AI agent platform integrating approved models and APIs, without model training, to support orchestration, tool use, guardrails, and evaluation.
Execute software solutions across design, development, and technical troubleshooting, breaking down complex problems beyond routine approaches.
Build and manage cloud-native infrastructure on AWS using scalable compute and event-driven patterns, including containers on ECS, serverless where appropriate, and event or messaging services such as EventBridge, SNS, and SQS, to support agent runtimes and workflows.
Develop secure, high-quality production code in Python and Java; review, debug, and improve code written by others.
Deliver end-to-end full-stack capabilities, including backend services and APIs and React-based user interface components where needed for agent-driven applications.
Build and maintain service interfaces and integrations, including REST APIs, authentication and authorization, and audit and logging patterns, for agent-driven applications.
Build reusable agent components, including prompting patterns, tool connectors, policy and guardrail hooks, and logging and tracing, to accelerate delivery across teams.
Automate build, deployment, testing, and release processes within the software development life cycle toolchain, including continuous integration and delivery, controls, runbooks, and change management.
Drive team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality and delivery speed while enforcing validation standards for secure coding, peer review, and automated testing.
Mentor and guide engineers through design reviews, code reviews, and engineering best practices, and contribute to continuous improvement.
Monitor platform and agent-service health, including latency, reliability, and cost; identify and resolve performance and stability issues; and eliminate recurring issues through root-cause fixes or automated remediation.

Desired Qualifications

Experience in agentic AI or building AI agents using existing models.
Experience with Model Context Protocol development and tool-integrated agent architectures.
Experience with cloud-native design and delivery on AWS.
Strong hands-on AWS experience across compute, eventing and messaging, and observability.
Strong hands-on Java and Python experience.
Knowledge of Adobe Experience Manager, including author and publish workflows, components and templates, and integrations and APIs.
Experience with continuous integration and delivery and production operations, including monitoring, incident support, and root-cause analysis, in enterprise environments.

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 at JPMorganChase within the Commercial & Community Banking, Marketing Tech you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities:

Design, build, and maintain an AI agent platform that integrates with approved models/APIs (no model training), supporting orchestration, tool-use, guardrails, and evaluation.
Execute creative software solutions across design, development, and technical troubleshooting; break down complex problems beyond routine approaches.
Build and manage cloud-native infrastructure on AWS using scalable compute and event-driven patterns (e.g., containers on ECS, serverless where appropriate, event/messaging with EventBridge/SNS/SQS) to support agent runtimes and workflows.
Develop secure, high-quality production code in Python and Java; review, debug, and improve code written by others.
Deliver full-stack capabilities end-to-end, including backend services/APIs and React-based UI components where needed for agent-driven applications.
Build and maintain service interfaces and integrations (REST APIs, authN/authZ, audit/logging patterns) required for agent-driven applications.
Build reusable agent components (prompting patterns, tool connectors, policy/guardrail hooks, logging/tracing) to accelerate delivery across teams.
Automate build, deployment, testing, and release processes within the SDLC toolchain (CI/CD, controls, runbooks, change management).
Drive team adoption of enterprise-authorized AI-assisted engineering practices, improving code quality and delivery speed while enforcing validation standards (secure coding, peer review, automated testing).
Mentor and guide engineers through design reviews, code reviews, and engineering best practices; contribute to continuous improvement.
Monitor platform and agent service health (latency, reliability, cost); identify and resolve performance and stability issues; eliminate recurring issues via root-cause fixes or automated remediation.

Required qualifications, capabilities, and skills:

Formal training or certification in software engineering concepts and 5+ years of applied experience.
Hands-on experience delivering system design, application development, testing, and operational stability for production systems.
Advanced proficiency in Python and/or Java (strong preference for both).
Working knowledge of React (and modern frontend fundamentals) to contribute to or lead delivery of full-stack features.
Demonstrated end-to-end (“full stack”) engineering across services/APIs, integrations, CI/CD automation, and production operations.
Practical, hands-on AWS experience building cloud-native services (compute, storage, networking, IAM, observability).
Proficient across all aspects of the Software Development Life Cycle.
Advanced understanding of agile methodologies including CI/CD, application resiliency, and security-by-design.
Demonstrated experience leading effective use of approved AI-assisted software development tools (coding, code review, test acceleration, troubleshooting), including setting validation expectations for correctness, performance, and security.
Strong understanding of responsible AI use in engineering workflows (data sensitivity, secure handling of inputs/outputs, resiliency and security expectations); experience coaching engineers on safe, compliant adoption.
Experience mentoring engineers and leading code/design reviews in an enterprise environment (e.g., financial services).

Preferred Qualifications, Capabilities, and Skills:

Experience in Agentic AI / building AI agents using existing models.
Experience with MCP development and tool-integrated agent architectures.
Experience with cloud-native design and delivery on AWS.
Strong hands-on AWS experience across compute, eventing/messaging, and observability.
Strong hands-on Java and Python experience.
Adobe Experience Manager (AEM) knowledge (author/publish, components/templates, integrations/APIs).
Experience with CI/CD and production operations (monitoring, incident support, root-cause analysis) in enterprise environments.

About the Company

JP Morgan Chase

Runs a consumer bank and an investment bank

JPMorgan Chase runs the largest bank in the United States, which serves households and small businesses as Chase and large companies, governments, and investors as J.P. Morgan. During the 2008 financial crisis it bought the failing investment bank Bear Stearns and lender Washington Mutual, and in 2023 it took over First Republic Bank after regulators seized it. Chase takes deposits and earns interest on credit cards, mortgages, and car loans, while the investment side earns fees for advising on mergers, raising money for companies, trading stocks and bonds, and managing wealth. Formed in a 2000 merger, JPMorgan Chase traces its roots to 1799 and is based in New York City.

Company size: 10,001+
Company stage: IPO
Headquarters: New York City, New York
Founded: 1799

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