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Lead Software Engineer - Python, Credit Trading Technology

JPMorgan Chase
Chicago, USA
Full-time
Lead · 5+ years experience
Onsite
Discovered 1 weeks ago
AWSPythonCommunication

Full Job Posting

Preferred qualifications, capabilities, and skills

  • Experience building scalable AI platforms for trading or financial services
  • Experience modernizing existing systems using AI-assisted engineering tools
  • Experience supporting electronic trading technology or other high-performance financial systems
  • Knowledge of Credit Markets, Sales and Trading, or related financial markets technology
  • Experience with AWS-based architectures in production environments

Qualifications

  • Formal training or certification on software engineering concepts and 5+ years of applied experience
  • Advanced hands-on experience with Python and delivering system design, application development, testing, and operational stability
  • Hands-on experience with artificial intelligence components and their application within modern technology stacks
  • Practical cloud-native experience, including foundational knowledge of AWS services and cloud engineering practices
  • Proficiency in automation and continuous delivery methods and all aspects of the Software Development Life Cycle
  • Advanced understanding of agile methodologies, CI/CD, application resiliency, and security
  • Demonstrated technical proficiency across modern software engineering disciplines such as cloud, artificial intelligence, and machine learning
  • Experience working with business and product partners to translate requirements into scalable technical solutions
  • Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
  • Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
  • Strong communication skills with the ability to collaborate effectively across technical, Product, and business team

Responsibilities

  • Executes creative software solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions or break down technical problems
  • Builds and supports AI-enabled systems to expand electronic trading capabilities, including scalable AI platforms and modernization of existing systems using approved AI tooling
  • Partners directly with Credit Sales and Trading and Product teams to understand business strategy and deliver impactful technical solutions
  • Architects and builds new systems hands-on using modern engineering practices and develops secure, high-quality production code while reviewing and debugging code written by others
  • Identifies opportunities to eliminate or automate remediation of recurring issues to improve operational stability and partners with support teams to ensure production reliability and timely issue resolution
  • Leads evaluation sessions with external vendors, startups, and internal teams to assess architectural designs, technical capabilities, and applicability within existing systems and information architecture
  • Leads communities of practice across Software Engineering to drive awareness and adoption of new and leading-edge technologies
  • Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
  • 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.

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