Summary
Daniel Romero is a Founding AI Robotics Engineer with a 14-year professional track record and deep, hands-on experience scaling AI and automation systems from research to production. He has optimized LLM and vision workloads with custom Triton GPU kernels and mixed-precision training, built large-scale RAG platforms powering 50M+ documents, and deployed hybrid retrieval and agentic pipelines using LangChain, Qdrant, and FastAPI. His background spans SRE and platform engineering—designing multi-cloud Kubernetes infrastructure, observability, and MLOps—which gives him a rare end-to-end fluency across hardware, model performance, and production services. Based in Ceará, Brazil, he combines stealth startup founding work in robotics with teaching AI and software engineering, and has a long history of transforming legacy systems into cloud-native, performant platforms. An early sysadmin turned ML engineer, he’s comfortable digging into GPU profilers one day and building developer-facing RAG demos the next, bringing pragmatic optimizations that cut latency and compute costs.
14 years of coding experience
24 years of employment as a software developer
Machine Learning by University of Washington on Coursera, Machine Learning by University of Washington on Coursera at University of Washington
Alura
Mathematics for Machine Learning on Coursera, Mathematics for Machine Learning on Coursera at Imperial College London
Applied Data Science with Python on Coursera, Applied Data Science with Python on Coursera at University of Michigan
Machine Learning and Data Science, Machine Learning and Data Science at Zero To Mastery Academy
Higher Systems for Internet, Higher Systems for Internet at CET
English, Portuguese