Agents and orchestration
Reliable agentic systems with LangGraph, memory, tool calling, human-in-the-loop, and deterministic workflows.
Machine Learning Specialist · Agentic AI Engineer
Building AI systems that make it to production.
I design agents, RAG systems, and cloud-native machine learning platforms that turn complex processes into useful, secure, and observable solutions.
7+years in technology and AI
2×AWS Certified
BRSalvador, Brazil
About me
I am a Machine Learning specialist and software engineer working at the intersection of applied AI, distributed systems, and cloud.
My current focus is designing reliable agentic systems that combine conversational intelligence with deterministic workflows, enterprise integrations, RAG, and production-ready infrastructure.
I work across the entire lifecycle: architecture, prototypes, agents and APIs, asynchronous processing, deployment, testing, monitoring, and continuous improvement.
What I do
From idea to reliable system.
Pragmatic engineering for intelligent products that need to work in the real world.
RAG and knowledge
Semantic search, embeddings, citations, and enterprise knowledge bases with AWS Bedrock and OpenSearch.
MCP and integrations
Secure integration layers connecting agents to business systems, APIs, and corporate data.
Cloud and production
Serverless and event-driven architectures with testing, CI/CD, observability, and infrastructure as code.
Selected projects
Products, agents, and research in code.
01020304
Enterprise knowledge · Decision-making
Enterprise RAG Assistant
I build assistants that organize internal knowledge and help teams find reliable answers to make decisions and act faster.
Operations · Service desk
Hybrid Service Desk Agent
I develop agents that make support more consistent, shorten response times, and help operations route every request with greater clarity.
Field operations · Voice
Voice Field Service Copilot
I create voice copilots that support field teams, reduce administrative work, and keep the right information available during service delivery.
Audio intelligence · UFBA thesis
violence-detection-acoustic-scenes
Research in audio intelligence and deep learning to recognize complex patterns in real acoustic scenes, at the intersection of signals, learning, and computational perception.
Current research · MBA USP/Esalq
How LLMs can reveal structure in unstructured data.
In my Data Science MBA, I investigate how language models can expand the analysis of unstructured data and generate useful knowledge for complex operations.
EmbeddingsTopic ModelingClusteringExplainable AI
Previous research at UFBA: my Electrical Engineering thesis on violence detection in acoustic scenes using deep learning. View project
Experience
DXC Technology
Machine Learning Specialist
Enterprise GenAI, RAG, NLP, and agent solutions integrated with corporate knowledge and operational systems.
Ford Motor Company
Machine Learning Software Developer & Researcher
Deep learning for acoustic events, connected-vehicle analytics, and software engineering for data at scale.
Federal University of Bahia
Machine Learning Researcher
Research in audio intelligence, acoustic event classification, and convolutional neural network models.
Core stack
PythonLangGraphAWS BedrockMCPFastAPIPostgreSQLOpenSearchDockerTerraformNext.js
Beyond technology
Curiosity is also a form of engineering.
My curiosity about technology began at home, through computers, science fiction, and conversations with my father about artificial intelligence. That story also shaped my commitment to expanding access to technology education for Black and underserved young people.
That combination brought me to AI: turning curiosity into systems that learn, connect knowledge, and help people make better decisions.
I hold a degree in Electrical Engineering from UFBA and a technical degree in Industrial Automation from IFBA. I am currently pursuing an MBA in Data Science at USP/Esalq.
Open to connections and meaningful conversations