Lead Software Engineer - Java/Python/AI/AWS

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.