Anirudh Rao

About Anirudh Rao

As a Machine Learning Engineer at Apple and Master\'s student at Georgia Institute of Technology, I architect and deploy production-scale AI systems that drive meaningful business impact and enhance customer experiences. With over 2 years of experience shipping ML solutions that have generated ~$180M in annual revenue, I specialize in building sophisticated generative AI systems, large language model architectures, and retrieval-augmented generation (RAG) pipelines designed to serve millions of customers.My expertise spans the full ML lifecycle - from designing high-performance document retrieval systems managing 100K+ files with over 98% accuracy, to building comprehensive evaluation frameworks with distributed LLM-as-judge infrastructure processing tens of millions of evaluations. I excel at transforming complex AI challenges into scalable, production-ready solutions through advanced prompt engineering, multi-model orchestration, and robust safety frameworks.Beyond technical execution, I\'m passionate about building teams and sharing knowledge. I actively mentor junior engineers, conduct technical interviews to strengthen our organization\'s capabilities, and develop internal tooling platforms that accelerate innovation across teams. My graduate studies at Georgia Tech complement my applied work, allowing me to bridge cutting-edge research with practical engineering solutions.I thrive at the intersection of academic rigor and industrial-scale deployment, turning state-of-the-art LLM technologies into reliable, impactful systems that solve real-world problems at scale.

Experience

1. Machine Learning Engineer

   Apple

   Oct 2024 — Present · Sunnyvale, CA, US

   Architected and developed a production-scale generative AI system designed for millions of customers, implementing advanced prompt engineering with intelligent context optimization, sophisticated content safety frameworks, and seamless multi-model orchestration for RAG-based applications, positioning the platform to drive substantial revenue growth through highly personalized recommendations and elevated customer engagement- Designed and implemented a comprehensive end-to-end evaluation infrastructure for large-scale generative AI systems, creating distributed LLM-as-judge frameworks capable of processing over evaluations, automated regression testing pipelines with extensive benchmark datasets, and quality validation systems, successfully reducing production issues by 40% through systematic quality gates and continuous monitoring- Led critical technical initiatives across knowledge base infrastructure and internal tooling platforms, designing a high-performance document retrieval system managing over files with over 98% accuracy for complex generative AI RAG systems, developing innovative prompt management and testing platforms with built-in version control capabilities, while mentoring junior engineers and conducting 10+ technical hiring interviews to strengthen team capabilities.

Education

• San José State University

  Transferred, Computer Science

• Dougherty Valley High School

  High School Diploma

• University of Illinois Springfield

  Bachelor of Science - BS, Computer Science

• Rutgers University

  Biotechnology

  2019 — 2020

• Georgia Institute of Technology

  Master of Science - MS, Computer Science - Specialization in Artificial Intelligence