Software Engineer III - Python Developer + AWS + LLM / Gen AI

About this role

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. 

As a Software Engineer III Python Developer +  LLM / Gen AI at JPMorgan Chase within Commercial & Investment Bank - Global Banking, you'll serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.

Job responsibilities

• Executes software solutions which integrate Gen AI models with Enterprise workflows, including design, development, and technical troubleshooting. Demonstrates the ability to think beyond routine or conventional approaches to build solutions or break down technical problems
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
• 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.
• Builds data pipelines that clean, transform, and aggregate data from disparate sources
• Creates secure and high-quality production code and maintains algorithms that integrate seamlessly with appropriate systems
• Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
• Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data se...