Software Engineer II -AI-Native - Branch Technology

You’re ready to gain the skills and experience needed to grow within your role and advance your career — and we have the perfect software engineering opportunity for you. As a Software Engineer II at JPMorganChase within the Corporate Sector - Employee Platform team, you are part of an agile team that works to enhance, design, and deliver the software components of the firm’s state-of-the-art technology products in a secure, stable, and scalable way. As an emerging member of a software engineering team, you execute software solutions through the design, development, and technical troubleshooting of multiple components within a technical product, application, or system, while gaining the skills and experience needed to grow within your role. You will work on a focused engineering team delivery features for every JPMorganChase retail branch. As a Software Engineer on the Branch Technology team, you help build the software-defined platform that manages and automates technology across the Retail Branch network. You work across the stack, own features end to end, and stay close to the architecture while learning from senior engineers who set a high bar and move fast. This team is modernizing a traditional C#, .NET, and PowerShell estate into an agentic-first platform, and you will be part of that shift. You do not need to arrive as an AI expert - you need to be a strong engineer who is excited to build with AI. You will write production Python, integrate tools and services, and work AI-native by default, using AI-assisted development across the lifecycle to ship faster. Job responsibilities Design, build, test, and ship production code for the branch automation platform, owning features from idea to deployment. Write clean, well-tested Python; contribute across C#/.NET/PowerShell as the team modernizes the existing estate. 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 integrate automation and tooling, including early agentic workflows and MCP tool integrations, under clear architectural direction. 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 Use enterprise-authorized AI capabilities in your daily workflow, validating outputs and handling operational data per sensitivity and security standards. Debug across the stack, consider upstream/downstream impact, and raise issues early; execute to compliance, risk, resiliency, and security expectations. Collaborate closely in a lean team, learn quickly, and raise the engineering bar through code reviews and shared practices. Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 2+ years applied experience Hands-on experience shipping production software, with strong fundamentals in data structures, algorithms, and system design. Proficiency in Python (or strong proficiency in another modern language with the ability to ramp on Python quickly). Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs. Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations. Solid understanding of testing, code review, source control, and CI/CD. Experience debugging and operating services in a cloud environment (public or private cloud). Curiosity and enthusiasm for working AI-native: willing to use AI-assisted development tools (e.g., VS Code, GitHub Copilot, Claude Code) and to learn agentic patterns on the job. Sound judgment with AI-assisted output: able to review and validate recommendations before use and escalate when uncertain. Preferred qualifications, capabilities, and skills Exposure to C#, .NET, or PowerShell. Any hands-on experience with LLM-integrated or agentic systems (coursework, side project, or production), or multi-agent frameworks (Google ADK, LangGraph, AutoGen). Familiarity with MCP (Model Context Protocol) tool integration or tools-first API design patterns. Interest in infrastructure/platform engineering and automation at scale.