Drimva

JPMorgan Chase

Full-Stack Software Engineer III (Python, React, Playwright)

$138K–$185K

Jersey City, NJ · Full-time · Posted Oct 19, 2026

Experience
3+ years of applied experience; no alternative stated.
Education
Formal training or certification on software engineering concepts.

About the job

Builds and maintains secure, scalable full-stack software for Payments Technology, including application development, testing, and system design.

What you'll do

  • Designs, develops, troubleshoots, and maintains production software and algorithms.
  • Creates architecture and design artifacts and analyzes data to improve applications, coding practices, and system architecture.
  • Uses enterprise-authorized AI coding tools to support development and automation, validating outputs for correctness, quality, and security.

What they ask for

  • Formal training or certification in software engineering concepts and 3+ years of applied experience.
  • Practical experience in system design, application development, testing, and operational stability.
  • Experience developing, debugging, and maintaining code in a large corporate environment using Python, React.JS, and database querying languages; experience with Playwright/Pytest.
  • Hands-on experience with enterprise-authorized AI-assisted development tools and ability to evaluate and refine outputs; understanding of responsible AI use, SDLC, agile methods, CI/CD, resiliency, and security.

Nice to have

  • Experience in payment processing; familiarity with modern front-end technologies and cloud technologies.
  • Familiarity with LLMs, including ChatGPT and Google Bard, and exposure to LangChain, RAG, or LLM fine-tuning.

Pay

USD 137,750.00-$185,000.00 base salary

Tools

Python, React.JS, Playwright, Pytest, GitHub, Copilot, Claude Code, LangChain

AI focus

Uses enterprise-authorized AI tools for code generation and refactoring, unit tests, documentation, troubleshooting, and development automation; validates outputs and applies responsible-use and security practices. Preferred exposure includes LLMs, RAG, and fine-tuning.

Model-written summary · the original posting is authoritative

First seen Oct 28, 2026 · Last verified Nov 4, 2026 (UTC)