Automation & AI Engineer focused on building intelligent systems that connect
data, business processes, APIs, documents, and machine learning models.
I'm Guilherme Vieira, an Automation and AI Engineer working at the intersection of artificial intelligence, data architecture, mathematical modeling, and process automation.
I design and develop solutions that transform complex business operations into intelligent, scalable, and data-driven systems.
My work involves building AI agents, RAG architectures, automation pipelines, machine learning models, APIs, and data workflows using technologies such as Python, FastAPI, Docker, MongoDB, SQL Server, RabbitMQ, OpenAI APIs, vector databases, and local LLMs.
I am especially interested in applying machine learning, statistical reasoning, optimization, retrieval systems, and intelligent automation to real business environments.
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Development of AI agents, RAG systems, LLM-based applications, semantic search pipelines, embeddings, vector databases, and intelligent assistants. |
Machine learning models, statistical analysis, data pipelines, feature engineering, model evaluation, and data-driven decision support. |
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Process automation, RPA architectures, message queues, API integrations, workflow automation, and backend services. |
Design of scalable architectures integrating APIs, databases, documents, business rules, AI models, and operational systems. |
- Building AI agents for business process automation
- Designing RAG architectures with retrieval quality, source traceability, and metadata filtering
- Creating data pipelines for structured and unstructured data
- Applying machine learning and statistical modeling to business problems
- Developing backend services and APIs with Python and FastAPI
- Exploring local LLMs, vector databases, embeddings, and autonomous workflows
AI Agents → intelligent assistants connected to APIs, databases, and documents
RAG Systems → retrieval pipelines with embeddings, metadata, and source traceability
Automation Pipelines → RPA, RabbitMQ, MongoDB, APIs, and backend orchestration
Data Products → structured datasets, analytics layers, and decision-support systems
ML Solutions → supervised, unsupervised, and experimental learning models