Hannah M. Smith

About Hannah M. Smith

I design and build AI-native systems and machine learning infrastructure, with deep specialization in Generative AI, Retrieval-Augmented Generation (RAG), and large language model (LLM) deployment at scale. My focus is practical: how do we go from prototype to production, and how does what we\'re building actually drive value?At Red Hat, I lead internal GenAI initiatives including LLM toolchains and RAG-based knowledge assistants that enhance operational efficiency and enterprise knowledge workflows. I work across the full stack, from modeling and evaluation to asynchronous APIs, vector search, and scalable system design.My background includes advanced analytics and optimization in the consumer finance sector. Today, I operate at the intersection of data, infrastructure, and product, bridging the gap between cutting-edge ML research and real-world deployment.Focus areas:Generative AI (LLMs, RAG, LangChain, LangGraph, prompt engineering)Machine learning engineering and API development (FastAPI, Pydantic, async Python)Vector search and embedding workflows (PostgreSQL with pgvector)Model evaluation, observability, and responsible AI practicesApplied AI product development and infrastructure-aware designNative English. Fluent in Spanish. Conversational in French and Portuguese.

Experience

1. Senior Data Scientist

   Red Hat

   Jul 2022 — Present

Education

• The University of North Carolina at Chapel Hill

  Bachelor of Arts, Economics, Political Science and Government Minor: Spanish