AI Prototyping Engineer

Brady Martz and Associates PC

  • St. Louis, MO
  • 1 day ago

    Highlights

    This role bridges business users and the software development team by rapidly testing concepts, building functional solutions, and preparing successful prototypes for efficient transition into production. Essential Position Responsibilities: Turn early-stage business ideas and loosely defined concepts into working prototypes using AI-enabled development tools and rapid experimentation.

    Numbers & Facts

    LocationSt. Louis, MO

    Description

    The AI Prototyping Engineer is responsible for turning emerging business ideas into working, deployable prototypes using AI-enabled development tools. This role bridges business users and the software development team by rapidly testing concepts, building functional solutions, and preparing successful prototypes for efficient transition into production.

    Essential Position Responsibilities:

    • Turn early-stage business ideas and loosely defined concepts into working prototypes using AI-enabled development tools and rapid experimentation.
    • Partner with business users, AI prototypers, and developers to clarify objectives, translate needs into functional solutions, and determine when a prototype has sufficiently proven a concept.
    • Build and refine AI-enabled features, including prompt design, agent workflows, API integrations, and methods for evaluating the quality and reliability of AI-generated output.
    • Package successful prototypes for effective handoff to the development team, using clear structure, version control, and documentation so production development can build on the work rather than restart it.
    • Apply firm data, privacy, and security practices during prototyping so solutions can transition appropriately to live firm data and production environments.
    • Use AI tools as a primary part of the prototyping and development process while independently reviewing, testing, and validating all generated output before it is relied upon.
    • Evaluate prototypes critically, identifying limitations, edge cases, and risks and determining when additional refinement is valuable versus when the concept is ready for handoff or should be discontinued.
    • Stay current with rapidly evolving AI development tools and techniques and apply new capabilities where they can improve speed, quality, or business value.

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