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Python Backend Software Engineer III- ML

Summary

Designs and builds scalable Python backend services and APIs for JPMorgan’s global payments platform, using MongoDB, NoSQL, and SQL databases while leveraging enterprise AI coding tools.

As a Software Engineer III at JPMorganChase within the Payments Technology 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

  • Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems

  • Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems

  • 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.

  • Design and develop scalable backend services and APIs using Python to support global payments platform capabilities
  • Build and maintain data persistence layers leveraging MongoDB, NoSQL, and relational databases (SQL/RDS) to ensure high availability and performance
  • 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

  • Hands-on practical experience in system design, application development, testing, and operational stability
  • Experience developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
  • Proficiency in Python for backend service and API development, with hands-on experience in MongoDB, NoSQL, and relational databases (SQL/RDS)
  • 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

  • Experience applying machine learning concepts or integrating machine learning models into backend platform services
  • Exposure to developer platform engineering, internal tooling, or platform-as-a-service patterns
  • Familiarity with cloud infrastructure (AWS, Azure, or GCP) and containerization technologies such as Docker or Kubernetes
  • Experience with CI/CD pipelines and infrastructure-as-code practices
  • Knowledge of payments domain concepts, including clearing, settlement, or liquidity management