Sr Lead Software Engineer - Cafe

JPMorgan Chase JPMorgan Chase · Banking · Houston, TX +1 · Corporate Sector

This role focuses on driving the adoption and governance of AI-assisted engineering practices within a large enterprise. The Senior Lead Software Engineer will guide teams on using AI tools for code quality, delivery speed, and operational outcomes, while ensuring responsible AI use, data sensitivity, and security compliance. The role involves applying knowledge of SDLC toolchains and AI capabilities to improve automation at scale, and coaching others on compliant usage.

What you'd actually do

  1. Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain.
  2. Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale.
  3. Regularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors
  4. Develops secure and high-quality production code, and reviews and debugs code written by others
  5. Drives decisions that influence the product design, application functionality, and technical operations and processes

Skills

Required

  • Formal training or certification on software engineering concepts and 5+ years applied experience
  • Hands-on practical experience delivering system design, application development, testing, and operational stability
  • Advanced in one or more programming language(s)
  • Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.
  • Ability to tackle design and functionality problems independently with little to no oversight
  • Practical cloud native experience
  • Ansible Automation experience
  • heavy Windows and Linux
  • Advanced knowledge in Enterprise automation and configuration management (System Center Configuration Manager, Ansible)
  • Advanced knowledge in Hardware Architecture (performance testing, monitoring, operations)
  • Advanced knowledge in Hardware Benchmarking (program management, network management)
  • Advanced knowledge in Design (compliance, security)
  • Advanced knowledge in Network Engineering (planning, provision)
  • Advanced understanding of business technology drivers and their impact on architecture design, performance and monitoring, best practices and JPMC policies
  • Lead upgrades, maintenance and configuration of SCCM infrastructure; Install, configure, test and maintain SCCM OSD, application software, compliance baselines and patch management
  • Proficiency with automation via Power Shell

Nice to have

  • Hybrid Cloud with AWS/Azure experience
  • PKI and PCI DSS experience
  • Active Directory experience
  • Experience using source code tools to store code, scripts and configurations (GitHub)
  • comfortable building/deploying micro service based solutions

What the JD emphasized

  • Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (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 senior engineers/leads on compliant usage patterns and controls.