Lead Software Engineer - Ai/ml Data Platforms - AI Engineer - Python

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

Lead Software Engineer for AI/ML Data Platforms at JPMorgan Chase, focusing on building production-grade AI/ML applications and Generative AI agents. The role involves advancing experiments, developing scalable platforms, and ensuring operational stability, primarily serving AI Research and MLCoE teams within a fintech domain.

What you'd actually do

  1. Works closely with Data Scientists and AI Researchers to advance experiments into more robust, scalable, highly optimized production-grade apps.
  2. Develops and writes software applications for AI/ML platforms as well as building Generative AI based applications including Agents.
  3. Utilizes creative problem-solving skills to design, develop, and troubleshoot technical solutions, thinking beyond conventional approaches to innovate and resolve complex technical challenges.
  4. Proactively identifies opportunities to streamline, eliminate, or automate the remediation of recurring issues and developer challenges, enhancing the operational efficiency and excellence of software applications and systems.
  5. Leads evaluation sessions (cross-team) to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture

Skills

Required

  • Full stack development experience
  • Infrastructure as Code development
  • system design
  • application development
  • testing
  • operational stability
  • Python
  • automation
  • continuous integration, delivery, and testing (CI/CD/CT)
  • Software Development Life Cycle (SDLC)
  • Model Development Life Cycle (MDLC)
  • agile methodologies
  • architectural frameworks
  • platform development

Nice to have

  • AI tools to enhance productivity
  • Terraform
  • experience in / exposure to a major business facing integrated application environment
  • working with business facing developers

What the JD emphasized

  • core technical contributor
  • production-grade apps
  • Generative AI based applications including Agents
  • platform development

Other signals

  • AI Research (AIR) and the Machine learning Centre of Excellence (MLCoE)
  • advance experiments into more robust, scalable, highly optimized production-grade apps
  • building Generative AI based applications including Agents
  • platform development and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)