Data

JPMorgan Chase & Co.•Plano, TX

About The Position

As a Lead Software Engineer at JPMorgan Chase within the Consumer and Community Banking - Risk Technology Portfolio team, you will be part of an agile team that builds and delivers trusted technology products in a secure, stable, and scalable way. You will take ownership of technical deliverables, contribute to design decisions, and work across cloud, data, and machine learning domains to solve real business problems.

Requirements

Formal training or certification on software engineering concepts and 5+ years applied experience
Hands-on practical experience delivering system design, application development, testing, and operational stability
Advanced in one or more programming language(s); Python or Java
Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
Proficient in all aspects of the Software Development Life Cycle
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
In-depth knowledge of the financial services industry and their IT systems

Nice To Haves

Experience in Agentic AI, MCP development & cloud native
Java/Python/AWS) having data engineering skillset is a plus

Responsibilities

Design, build, and maintain platform components that support end-to-end ML model lifecycle — from development and training to deployment and monitoring
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards.
Build and manage cloud-based infrastructure on AWS (including Databricks, EMR, ECS, and S3) to support model training and serving workloads
Automate model deployment, testing, and release processes within the SDLC/MLOps toolchain
Support Platform team in developing/building automated solutions
Monitor platform health and model serving infrastructure; identify and resolve performance and stability issues
Apply AI-assisted development tools and best practices to improve code quality and delivery speed
Contribute to a team culture of diversity, inclusion, and continuous improvement