I'm a Machine Learning (Operations) Engineer and Academic Researcher from Brazil 🇧🇷, building cloud-native, open source AI/ML solutions.
My stack is rooted in open technologies: Linux as the foundation, containers orchestrated with Kubernetes and Docker, infrastructure provisioned with Terraform, and ML pipelines that are reproducible, observable, and community-auditable by design. I believe the future of AI infrastructure is open and I actively work to build it that way.
- 🔬 Research focus: Model lifecycle governance, FinOps for ML, and cost-efficient training at scale
- 🏗️ Currently deepening: IaC with Terraform, container-native ML workflows, and Linux ecosystem tooling
- 🎓 Educator at heart: I enjoy sharing what I learn; through writing, mentoring, and open documentation
- 🤝 Open to: Collaborations on open source MLOps projects, research, and community-driven AI initiatives
- ✍️ Writing: I document my learnings on Medium; from Docker deep-dives to FinOps and tech certifications
- 💬 Ask me about: Python, PySpark, Kubernetes, Docker, Azure, Databricks, and MLOps best practices
| Languages & OS | Containers & Orchestration | Cloud & IaC | Data & ML |
|---|---|---|---|
 Python |
 Docker |
 Azure |
 PySpark |
 Linux |
 Kubernetes |
 AWS |
 Databricks |
 Bash |
 Terraform |
 Datadog |
 Pandas |
 Scikit-learn |
Increasingly building with and contributing to the open ecosystem:
Open source is a direction, not a destination; I work across proprietary and open stacks and believe the best infrastructure borrows the best of both worlds.
- Prompt Engineering, Padrões de Referência e Sequências de Raciocínio
- A Área de Dados no BR: Uma Leitura Crítica a Partir do State of Data 2026
- Estratégias de Otimização e FinOps Aplicado ao Treino de Modelos
- Techwriting e Cartografia de Controvérsias no Onboarding
- Antes do Badge: Uma Jornada Rumo às Certificações Profissionais em TI
