Senior Lead Software Engineer - AI

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

Senior Lead Software Engineer focused on building and delivering AI-driven technology products within Corporate Technology at JPMorgan Chase. The role emphasizes driving GenAI adoption, accelerating innovation to production, and ensuring governance transparency by building scalable solutions and integrating AI-assisted engineering practices into the SDLC. Key responsibilities include technical guidance, secure code development, driving adoption of AI tools, building resilient services, and embedding governance and auditability.

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. Build and operate secure, resilient services that support discovery, reuse, and delivery at enterprise scale.
  3. Convert high-potential ideas into production deployments through stage-gated engineering execution.
  4. Embed governance, auditability, and controls into the full software lifecycle.
  5. Partner across architecture, security, SRE, testing, product, and data to deliver measurable business outcomes.

Skills

Required

  • Formal training or certification on software engineering concepts and 5+ years applied experience
  • Lead technical design and implementation for platform services and workflow integrations.
  • Build capabilities that improve discoverability of existing assets and ownership to reduce parallel delivery.
  • Deliver automation that improves engineering speed, first-pass quality, and operational reliability.
  • Implement telemetry for adoption, reuse, cycle time, quality, and value realization.
  • Create production readiness evidence for every rollout: resiliency, security, rollback, observability, and runbook completeness.
  • Support innovation funnel execution from intake through feasibility, acceleration, and production.
  • Mentor engineers and raise team standards in coding, testing, and operational excellence.
  • 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

What the JD emphasized

  • AI-assisted engineering practices
  • responsible AI use
  • AI-assisted software development tools

Other signals

  • GenAI adoption
  • innovation-to-production conversion
  • governance transparency
  • AI-assisted engineering practices
  • AI-assisted development and automation capabilities
  • responsible AI use