Fullstack Python Software Engineer III

JPMorgan Chase & Co.•Plano, TX

About The Position

This role involves executing software solutions, encompassing design, development, and technical troubleshooting, with an emphasis on innovative problem-solving beyond conventional approaches. The engineer will create secure, high-quality production code and maintain algorithms that operate synchronously with appropriate systems. Responsibilities include producing architecture and design artifacts for complex applications, gathering and analyzing data to drive improvements, and contributing to software engineering communities of practice. A key aspect of this role is leveraging enterprise-authorized AI coding assist tools to enhance code quality, delivery speed, and productivity, while ensuring outputs are validated through peer review, automated testing, and secure coding standards. The engineer will also apply knowledge of the Software Development Life Cycle toolchain, including AI-assisted development and automation capabilities, to maximize value from automation.

Requirements

3+ years experience Python/Java development.
Solid understanding of databases (SQL, DB2 or NOSQL)
Hands-on practical experience in system design, application development, testing, and operational stability
Proficient in coding in one or more languages
Exposure to financial products such as ACBS
Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
Solid understanding on messaging or streaming platforms like Kafka, Unified Message Bus etc.
Experience with containerization technologies (Docker, Kubernetes) and container orchestration in production environments
Exposure to cloud-native development and AWS services, particularly AWS EMR, Glue, ECS, EKS (Elastic Kubernetes Service) for deploying and managing containerized applications
Solid understanding of agile methodologies such as CI/CD, Bitbucket, Github, Application Resiliency, and Security etc.
Familiarity with AI-assisted development tools and agentic coding workflows (GitHub Copilot etc.) and understanding of GenAI fundamentals for developer productivity
Demonstrated knowledge of software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence etc.)
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.

Nice To Haves

Familiarity with modern data technologies
Exposure to cloud technologies

Responsibilities

Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
Creates secure and high-quality production code and maintains algorithms that run synchronously 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 sets in service of continuous improvement of software applications and systems
Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
Contributes to software engineering communities of practice and events that explore new and emerging technologies
Adds to team culture of diversity, opportunity, inclusion, and respect
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.