Software Engineer III - Full Stack

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Software Engineer III - Full Stack

senior · Technology / Software Development

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.
 

• 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
  
  Required qualifications, capabilities, and skills

• 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

  Preferred qualifications, capabilities, and skills

• 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.