AI Applications Engineer

Cirrus LogicAustin, TX

About The Position

As a member of the AI Applications team, you will help build the infrastructure, tools, and workflows that enable engineering teams to use AI effectively. You’ll partner with teams across engineering operations to design and improve AI-augmented workflows that increase efficiency and productivity. You’ll also help collect and analyze user feedback to continuously improve our AI platforms and agent harnesses. If you enjoy debugging complex problems, identifying inefficiencies, and building practical solutions in a fast-paced, collaborative environment, we invite you to apply.

Requirements

  • Bachelor’s degree in Electrical/Computer Engineering, Computer Science, related technical field, or equivalent practical experience.
  • 1+ years of relevant software engineering experience.
  • Strong software engineering fundamentals, including debugging, testing, and problem solving.
  • Interest in building, evaluating, and integrating AI-agent or LLM-enabled systems.
  • Familiarity with DevOps practices, CI/CD systems, and developer tooling.
  • Ability to analyze workflows, identify inefficiencies, and deliver practical improvements.
  • Effective written and verbal communication skills, with experience collaborating across teams.
  • Comfortable working in a fast-paced environment and managing competing priorities.
  • Demonstrated ability to take ownership and deliver results in ambiguous, rapidly evolving environments.
  • Strong bias toward action, paired with sound technical judgment and disciplined validation.

Responsibilities

  • Proactively identify high-value opportunities for AI-enabled workflow improvements.
  • Develop and maintain AI-enabled tools used across our Engineering Operations team.
  • Partner with engineering teams to integrate AI agents and AI-assisted workflows into their day-to-day work.
  • Build infrastructure that enables secure, scalable adoption of AI across operations.
  • Demonstrate new tools and capabilities, gather feedback, and support adoption.
  • Turn ambiguous needs into practical, scalable solutions that improve engineering efficiency and productivity.
  • Navigate obstacles, challenge assumptions, and drive decisions with limited direction.
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