LLM Systems Engineer
Alex Galedo
Building production-ready AI systems that transform complex ideas into intelligent products.
Explorer
Project Explorer
Category
Status
Engineering Philosophy
How I Build
Building Reliable AI Systems
Production AI requires engineering rigor. I build systems that are testable, observable, and predictable — treating models as components in a larger architecture rather than magic black boxes.
Product Thinking
AI features must solve real user problems. I start with the experience and work backward, ensuring every model, pipeline, and API serves a clear product purpose.
LLM Engineering
From prompt optimization to fine-tuning to RAG architecture, I apply structured engineering practices to the inherently unstructured world of language models.
Developer Tooling
The best AI systems are built with great tools. I invest in evaluation frameworks, debugging infrastructure, and developer workflows that make AI engineering predictable.
Continuous Learning
This field moves fast. I stay current through building, reading papers, and sharing what I learn with the community.
Shipping Practical Software
Research is valuable, but shipped products create impact. I prioritize pragmatic solutions that can be deployed, maintained, and iterated on in real-world environments.
About
Building the Infrastructure of Intelligence
I am an LLM Systems Engineer focused on the infrastructure layer of modern AI — the pipelines, evaluation frameworks, and orchestration systems that make production AI reliable and scalable.
My work spans RAG architecture, multi-agent orchestration, and developer tooling for LLM applications. I believe the next breakthroughs in AI will come not just from better models, but from better systems built around them.
I am currently exploring agentic workflows, production evaluation strategies, and the intersection of systems engineering with language model capabilities.