Software Engineer III - Full Stack

JPMorganChase•Jersey City, NJ

About The Position

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 JPMorganChase within the Corporate Sector, 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 will develop end-to-end AI-enabled products; own features across backend and frontend, deploy to cloud infrastructure, and integrate LLM/agent capabilities into production systems. The role values practical delivery, strong fundamentals, and clear collaboration.

Requirements

Formal training or certification on software engineering concepts and 3+ years applied experience
Strong Python (clean architecture, testing, performance awareness)
Solid React + modern JavaScript/TypeScript for functional, maintainable UIs.
Cloud-native development experience: containers + APIs/microservices + at least one major cloud (AWS preferred).
Hands-on experience building with LLMs and agentic AI (LLM APIs and/or frameworks such as Google ADK, LangGraph, or similar) production or real deployed work, not just prototypes.
Working knowledge of relational databases (PostgreSQL/Aurora), including schema design and query patterns.
Strong fundamentals: data structures, REST/APIs, Git, testing, debugging.
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.
Overall knowledge of the Software Development Life Cycle
Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security

Nice To Haves

Familiarity with the Model Context Protocol (MCP) or building tools/plugins for AI agents.
Experience with RAG, prompt engineering, evaluations, and/or production guardrails.
Kubernetes/EKS, AWS Lambda, and infrastructure-as-code.
Observability/tracing tooling (e.g., OpenTelemetry, Arize Phoenix).
Experience in regulated/enterprise environments with security and governance requirements.

Responsibilities

Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
Design and build clean, testable Python services and APIs; optimize for reliability and performance.
Build maintainable UIs using React and modern JavaScript/TypeScript.
Integrate LLM APIs and/or agent frameworks into real workflows and user experiences.
Develop cloud-native systems using containers, microservices, and AWS (preferred).
Work with PostgreSQL/Aurora: schema design, querying, and state/session management patterns.
Write tests, debug issues, review code, and ship iteratively in a fast-paced environment.
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.
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