As an ML Engineer and AI Solutions Architect, I design and deploy robust, end-to-end AI solutions. At Telecom Argentina, I lead the design of our private AI/ML infrastructure, building a hybrid, Kubernetes-based cloud for GPU-accelerated workflows with frameworks like OpenShift and Kubeflow. My focus is on creating scalable MLOps pipelines and integrating state-of-the-art models—including Generative AI—into secure and cost-effective business applications.
My hands-on expertise spans a range of applications, from engineering complex, CUDA-optimized speech analytics services and predictive maintenance models to developing computer vision systems for object detection. I specialize in advancing projects from initial baselines to cutting-edge model deployment, ensuring high performance and seamless integration.
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Python |
MATLAB |
Bash
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- Scientific computation
Numpy |
SciPy |
CUDA |
ROCm
- Data manipulation & visualization
Pandas |
Polars |
Matplotlib |
Seaborn - ML & Deep Learning
PyTorch |
LightningAI |
Scikit-Learn |
TensowFlow |
Keras |
Hugging Face
- Scientific computation
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AWS |
GCP |
OpenShift |
Kubeflow |
Git |
Kubernetes
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Docker |
Podman |
JupyterLab |
Colab |
VS Code |
FastAPI |
Flask |
Redis |
Kafka
