Sebastian Ramos Regalado · Staff AI EngineerI build AI agents, RAG and ML systems.Then I make them run in production.

Multi-agent system that executes multi-step work across business tools

Built

Multi-step work that spans APIs, databases and external services is hard to hand to a single model call. I designed and built a multi-agent system that reasons, plans and carries that work out.

Result

Carries multi-step tasks across APIs, databases and external services, with fewer failure cases in complex workflows.

• multi-agent systems
• tool use
• orchestration
• LangChain
• CrewAI

RAG system that answers from structured and unstructured sources together

Built

Useful answers often depend on both structured records and unstructured documents. I built a retrieval-augmented generation system that pulls from both in real time.

Result

Answers in real time, grounded in retrieved context from structured and unstructured data.

Vertex AI pipelines that take models from preprocessing to managed endpoints

Built

Training a model is only part of the job; it also has to run reliably in production. I designed and deployed Google Cloud Vertex AI pipelines that cover data preprocessing, training, inference and endpoint management.

Result

Serves real-time inference inside production systems, from data preprocessing to a managed endpoint on one repeatable path.

Real-time matching system built for noisy, incomplete data

Built

The hard part of this system was ambiguity: inputs that are noisy, incomplete or contradictory. I designed a modular real-time inference pipeline that combines computer vision, data ingestion, NLP processing and contextual matching.

Result

Matches in real time while handling ambiguous and incomplete inputs, with each component scaling independently.