Avis Budget Group: Enterprise AI Engineering & Enablement

Context

As an AI Solutions and Enablement Intern at Avis Budget Group, I work across problem discovery, software development, evaluation, and AI enablement. My role connects hands-on engineering with the people who will use the technology. Internal applications, business data, and implementation details are confidential; this overview focuses on my contributions and capabilities.

Problem

Turning a business need into a dependable AI application takes more than a working prototype. It requires clear requirements, reliable data, careful evaluation, and an experience that makes sense to the people using it.

Approach

I work with stakeholders to clarify the problem and translate it into a scoped product. I build and test the software, investigate data-quality issues, and communicate technical tradeoffs in business terms. I treat reliability and human judgment as design requirements throughout the work.

Capabilities

AI & Data EngineeringFull-Stack DevelopmentEvaluation & TestingCloud Application DeliveryStakeholder DiscoveryResponsible AI Enablement

Contributions

  • •Built full-stack AI applications and data pipelines, connecting backend engineering, data modeling, and user-facing interfaces.
  • •Developed automated tests and evaluation methods to check outputs, diagnose defects, and improve reliability.
  • •Collaborated with platform engineers on cloud application delivery, with clear ownership across application and infrastructure work.
  • •Translated technical findings into demonstrations and recommendations for business stakeholders.
  • •Designed and led practical AI workshops for teams in the US and UK, covering effective use, limitations, and responsible judgment.

Outcomes

  • ✓Delivered working software and evaluation tooling, taking ownership beyond the initial prototype.
  • ✓Identified additional opportunities through hands-on investigation and developed them into working capabilities.
  • ✓Created training materials and delivered live sessions that helped non-technical colleagues explore AI in their work.

Learnings

  • →Reliable AI starts with understanding the data and testing the assumptions behind the result.
  • →Effective delivery requires both technical ownership and close collaboration with business and platform teams.
  • →Clear explanations and practical learning experiences are part of the product, not an afterthought.