Senior Lead Software Engineer - Ai/ml & Data Platforms

JPMorgan Chase JPMorgan Chase · Banking · LONDON, United Kingdom · Commercial & Investment Bank

Senior Lead Software Engineer for AI/ML & Data Platforms at JPMorgan Chase in London. This role focuses on leading technical areas, driving innovation, and implementing AI-assisted engineering practices within the software development lifecycle. The engineer will be responsible for governance, decision-making, and ensuring responsible AI use, with a strong emphasis on AWS/public cloud applications and leading cross-functional teams.

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. Makes decisions that influence teams’ resources, budget, tactical operations, and the execution and implementation of processes and procedures
  4. Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
  5. Delivers technical solutions that can be leveraged across multiple businesses and domains

Skills

Required

  • software engineering leadership
  • system design
  • application development
  • testing
  • operational stability
  • AWS/public cloud based applications
  • leading effective use of enterprise-authorized AI-assisted software development tools
  • responsible AI use in engineering workflows
  • developing or leading cross-functional teams of technologists
  • hiring, developing, and recognizing talent

Nice to have

  • AI-assisted code review/refactoring
  • test acceleration
  • release readiness
  • incident/root-cause analysis
  • secure coding
  • peer review
  • automated testing
  • SDLC/TLM toolchain

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.