Lead Software Engineer - Java/Python/AI/AWS

JPMorgan Chase & Co.•Plano, TX

About The Position

As a Lead Software Engineer at JPMorganChase within Commercial & Community Banking, Marketing Tech, you will contribute to an agile team that builds and delivers secure, stable, scalable technology products. You will design, build, and maintain an AI agent platform using approved models and APIs, without training models, and deliver agent-driven applications and workflows across backend services, APIs, cloud infrastructure, and React-based user interfaces where needed. The role includes developing production-quality software in Python and Java, building cloud-native services on AWS, creating reusable agent components and integrations, and automating delivery through the software development lifecycle. You will help teams adopt approved AI-assisted engineering practices responsibly, mentor engineers, and maintain the performance, reliability, security, and cost effectiveness of platform services.

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 full-stack engineering across services and APIs, integrations, CI/CD automation, and production operations.
Practical, hands-on AWS experience building cloud-native services across compute, storage, networking, IAM, and observability.
Proficiency across all aspects of the Software Development Life Cycle.
Advanced understanding of agile methodologies, CI/CD, 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, 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.

Nice To Haves

Experience with Agentic AI or 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 and messaging, and observability.
Strong hands-on Java and Python experience.
Adobe Experience Manager (AEM) knowledge, including author/publish, components/templates, and integrations/APIs.
Experience with CI/CD and production operations, including monitoring, incident support, and root-cause analysis, in enterprise environments.

Responsibilities

Design, build, and maintain an AI agent platform integrating approved models and APIs, including orchestration, tool use, guardrails, and evaluation.
Design and deliver software solutions, troubleshoot technical issues, and address complex problems beyond routine approaches.
Build and manage cloud-native AWS infrastructure using scalable compute and event-driven patterns, including ECS, serverless services where appropriate, EventBridge, SNS, and SQS.
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 UI components where needed.
Build service interfaces and integrations, including REST APIs, authentication and authorization, audit, and logging patterns.
Create reusable agent components such as prompting patterns, tool connectors, policy and guardrail hooks, and logging and tracing capabilities.
Automate build, deployment, testing, and release processes within the SDLC toolchain, including CI/CD, controls, runbooks, and change management.
Drive adoption of enterprise-authorized AI-assisted engineering practices 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; contribute to continuous improvement.
Monitor platform and agent service health, including latency, reliability, and cost; resolve performance and stability issues and address recurring issues through root-cause fixes or automated remediation.

Benefits

Competitive total rewards package, including base salary determined by role, experience, skill set, and location.
Eligible roles may receive commission-based pay and/or discretionary incentive compensation in cash and/or forfeitable equity.
Comprehensive health care coverage.
On-site health and wellness centers.
Retirement savings plan.
Backup childcare.
Tuition reimbursement.
Mental health support.
Financial coaching.

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