Full Stack Software Engineer III - Network Perimeter & CDN

hackajob is collaborating with J.P. Morgan to connect them with exceptional professionals for this role.

JOB DESCRIPTION

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

As a Software Engineer III at JPMorgan Chase within the Network Services team, you 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
â Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
â Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
â 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.
â 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.
â Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
â Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
â Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture
â 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.
â 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.

Required qualifications, capabilities, and skills
â Formal training or certification on software engineering concepts and 3+ years applied experience
â Strong Python development skills, including writing well-tested, maintainable code
â Experience building and maintaining APIs using Django and/or FastAPI (or similar frameworks)
â Strong system design skills across distributed services, data stores (relational and No

SQL), and messaging systems (for example, RabbitMQ)
â Practical experience running services in the cloud, including Amazon Web Services patterns and services
â Solid understanding of secure software development lifecycle practices, continuous integration/continuous delivery, and operational excellence (monitoring, alerting, incident response, resiliency)
â Demonstrated ability to lead through influence via code reviews, technical decision-making, and mentoring within a team
â Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
â 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.
â Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of…