Lead Software Engineer - Python , Fast API , Aws

JPMorgan Chase JPMorgan Chase · Banking · Bengaluru, Karnataka, India · Corporate Sector

Lead Software Engineer role focused on enhancing and building technology products within an Infrastructure Platforms team. The role involves executing software solutions, designing, developing, and troubleshooting, with a strong emphasis on driving team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes. Responsibilities include creating secure production code, producing architecture artifacts, and applying knowledge of AI-assisted development tools within the SDLC. The role also requires gathering and analyzing data for continuous improvement and contributing to software engineering communities. A key aspect is demonstrating experience leading the effective use of approved AI-assisted software development tools and coaching engineers on responsible AI use.

What you'd actually do

  1. 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.
  2. 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.
  3. Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
  4. Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
  5. Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development

Skills

Required

  • Formal training or certification on infrastructure disciplines concepts and 5+ years applied experience
  • Hands-on practical experience in system design, application development, testing, and operational stability
  • Proficient in coding in Python and web framework work such as Django/FastAPI
  • Experience in developing, debugging, and maintaining code in a large corporate environment with Python and database querying languages
  • Strong experience with AWS cloud services and architecture
  • 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
  • Excellent analytical and logical reasoning skills, with the ability to solve complex problems efficiently
  • Overall knowledge of the Software Development Life Cycle
  • Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
  • Demonstrated knowledge of software applications and technical processes within a technical discipline

Nice to have

  • Experience with Terraform for infrastructure as code
  • Familiarity with modern front-end technologies such as ReactJS
  • Exposure to additional cloud technologies and services

What the JD emphasized

  • 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