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.