THE PERSON
BEHIND THE CODE.

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 ~$80M 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.