About Matheus
I am an AI & Data Engineer based in Brasília, Brazil. I build agents and the execution, evaluation, data, and cloud systems behind them.
In a production agent project, I evolved a production agent harness built around the Pi SDK. The runtime assembles context from declarative runbooks, controls tools and command execution, persists sessions, recovers interrupted delivery, and connects traces and user feedback to evaluation.
I also designed and implemented Smart Agent Factory, a custom Python graph and durable runtime for AI-built software. It combines human decisions, isolated worktrees, bounded execution, external validation, time travel, and evidence receipts in a resumable delivery workflow.
I use loop engineering to connect the model's tool loop, the harness control loop, the product feedback loop, and the evaluation loop that improves models, prompts, tools, and policy.
My work builds on experience in backend software, large-scale data platforms, and cloud infrastructure. I led technical direction across multiple data platform teams, supported shared lakehouse and orchestration capabilities, and worked on a Spark-to-Kubernetes migration that improved infrastructure efficiency.
AI engineering
As an AI & Data Engineer, I build agents, execution tooling, and the data and cloud infrastructure that supports them.
I evolved the agent harness's Pi-based runtime in TypeScript, connecting declarative runbooks and deterministic context preparation to governed tools, persistent sessions, execution policy, recovery, tracing, user feedback, and model evaluation.
Read case studyI connect OpenTelemetry and Langfuse traces, BigQuery feedback, model comparison, judge calibration, and instrumentation checks. The resulting loop uses production evidence to guide controlled changes to models, prompts, tools, and policy.
Read case studyI designed and implemented a custom Python graph and runtime with append-only state, checkpoints, leases, deduplication, transactional outbox delivery, budgets, bounded retries, human gates, isolated worktrees, external validation, time travel, and receipts.
Read case studyI build the infrastructure around AI services, including Terraform modules, Kubernetes deployment configuration, access controls, observability, and backup automation. I also create incremental pipelines, analytical models, and data-quality workflows with Python, BigQuery, Dataform, and Dataplex.
Read case studyAgent systems knowledge
I treat an agent as a governed software system. The harness controls the context, tools, state, budgets, evidence, and recovery around the model.
See this approach in a delivered systemI map how a request becomes a specification, plan, implementation, and verified result before choosing an agent topology. The manual decisions reveal where automation is safe and where human judgment is still required.
Planning, implementation, and validation run with clear authority boundaries. They exchange durable artifacts, so each stage receives the context it needs without inheriting an unbounded conversation.
Approved goals become sealed contracts with allowed change scope, runnable evaluations, hidden holdouts, and evidence receipts. A deterministic gate checks the work before model-based review begins.
Risk lanes, human decisions, budgets, retries, and resumable state determine how far a workflow can proceed on its own. Autonomy expands only when the system can show why its result should be trusted.
Reference architecture
The model reasons inside a harness that controls context, tools, state, permissions, budgets, human decisions, and acceptance.
Harness / control plane
Engineering capabilities
The disciplines I connect when building and operating AI systems.
Tool-using agents, multi-agent workflows, and retrieval-augmented generation. Execution controls, model evaluation, tracing, and feedback are part of the engineering work.
LangGraph, LangChain, Pydantic AI, Pi agent harness, LiteLLM, Langfuse, Claude Code
Ingestion, batch and streaming pipelines, analytical models, lakehouses, and data quality. The data foundations that support analytics and AI applications.
Spark, Airflow, Kafka, BigQuery, dbt, Dataform, Delta Lake, Apache Iceberg, Dataplex
Backend services, APIs, execution tooling, and reusable libraries. Typed interfaces, state management, validation, and failure handling connect agents to other systems.
Python, Go, TypeScript, SQL, FastAPI, Pydantic, SQLAlchemy
Infrastructure as code, containers, Kubernetes, CI/CD, and access controls, with monitoring and observability for AI and data workloads.
GCP, Kubernetes, Terraform, GitHub Actions, OpenTelemetry, Prometheus, Grafana
Experience
Projects, technical contributions, and engineering disciplines across my career.
AI & Platform Engineer
Build AI agents and execution tooling, evaluation and feedback pipelines, and reusable developer infrastructure across Python, TypeScript, data systems, and cloud services.
AI & Platform Consultant
Designed and built an end-to-end multi-agent delivery platform with specialized roles, human decision gates, artifact-based verification, resumable sessions, and isolated Git worktrees.
View case studyData Engineer Specialist to Technology Lead
Led technical direction across multiple data platform teams, supported shared lakehouse and orchestration capabilities, and worked on a Spark-to-Kubernetes migration that improved infrastructure efficiency.
View case studyKubernetes Developer
Built Airflow workflows for data acquisition, model training, and validation, plus the API layer for a Kubernetes-based machine learning platform.
View case studySenior Data Engineer
Optimized BigQuery queries, table design, capacity, and storage placement, reducing recurring monthly BigQuery cost by 25%.
View case studyData Science Specialist
Built MLOps pipelines and online inference services for recommendation and time-series applications, using Python, Kubernetes, Kubeflow, and automated delivery.
Data Scientist
Developed natural-language processing and recommendation applications, exposed models through Python inference APIs, and deployed microservices on Kubernetes.
Electronic Engineer and IT Trainee
Designed an embedded energy-monitoring system with LoRa connectivity, hardware and firmware, and a Raspberry Pi gateway using MQTT. Built sensor acquisition systems with ESP32 and supporting applications.
Credentials
IESB
Universidade de Brasília
The Linux Foundation
The Linux Foundation
HashiCorp
Contact
Tell me about your project and the challenge you are working through.