ML Systems Engineer
A Computer Engineering graduate from CEFET/RJ, I operate at the intersection of algorithmic abstraction and engineering rigor. My focus isn’t the hype, but building systems where AI acts as a predictable, scalable, and high-performance component.
I inspect the pipeline end-to-end: from hardware constraints to inference optimization.
Domain Optimization
My stack reflects the necessity of navigating between low-level performance and high-level abstraction. Whether the bottleneck lies in network latency or model architecture, I intervene.
- Backbone:
Python,C/C++,TypeScript,Rust(in progress). - Intelligence:
PyTorch,TensorFlow,RAG Architectures,LLM Agents,CUDA. - Reliability:
Docker,Kubernetes,AWS,NestJS,Astro.
Pragmatics Over Theory
I believe production-ready code is worth more than isolated metrics. In the real world, models die from lack of infrastructure, not lack of precision. My track record at Planium and across Deep Learning projects focuses on entropy reduction: transforming raw, chaotic data into actionable business signal.
When my editor is closed, my focus remains on systems analysis—whether dissecting complex game mechanics or exploring emerging hardware architectures.