python
Background
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Role: Full Stack Developer & Lead Developer of Synthesia & Avapre Projects
Expanded leadership scope beyond the Synthesia project to spearhead Avalia Preenche (Avapre), an advanced AI-powered document extraction system.
- Intelligent Pipelines: Engineered robust OCR and structured data extraction workflows using Pytorch, OpenCV and Large Language Models (LLMs), significantly reducing manual data entry for healthcare providers.
- Scalability: Optimized performance for high-volume processing in a critical healthcare environment.
- Legacy System Support: Identified and resolved critical bugs in their legacy platform, ensuring system stability and reliability while integrating new features.
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A Deep Learning solution designed to detect forged SMTP headers. This project addresses the scarcity of labeled datasets in cybersecurity by employing Synthetic Data Generation to train robust models capable of identifying sophisticated phishing attempts.
- Tech Stack: Python, PyTorch.
- Outcome: Published as a full paper at SBSeg 2025.
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Role: Full Stack & Synthesia Lead Developer
Promoted to the Innovation Sector to architect Synthesia, a bespoke AI platform for the supplementary healthcare market used by giants like Hapvida.
- RAG Architecture: Designed and implemented a Retrieval-Augmented Generation pipeline capable of processing diverse data sources with high precision.
- AI Agents: Developed customizable LLM-based agents integrated with external enterprise systems via API.
- Impact: Delivered a flexible, user-friendly toolset that accelerated AI adoption for clients, moving from concept to production-ready solution.
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Provider: Alura
Focused on the operationalization of ML. Developed interactive web-based dashboards using Plotly Dash to visualize model outputs, enabling stakeholders to interact with real-time predictions and data insights.
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Provider: Alura
Comprehensive 48-hour specialization covering the end-to-end Machine Learning lifecycle. Focused on data preprocessing, feature engineering, and deploying classification/regression models. Validated expertise in model evaluation metrics and production-ready ML workflows.
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Provider: Alura
Focused on sequential data architectures, I utilized PyTorch to design and implement Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), effectively addressing the vanishing gradient problem to maintain temporal dependencies in complex time-series and natural language tasks.
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Provider: Alura
Implementation of unsupervised learning techniques. Focused on partitioning data using algorithms like K-Means, evaluating cluster validity, and extracting patterns from high-dimensional datasets without prior labeling.
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Provider: Alura
Statistical analysis and predictive modeling. Applied Linear Regression to identify correlations between variables and quantify the impact of features on target outcomes using statistical significance tests.
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Provider: Alura
Deep dive into the mathematical mechanics of training. Explored the logic behind backpropagation, gradient descent, and the optimization of loss functions to adjust internal weights and ensure model convergence.
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Provider: Alura
Developed robust classification pipelines using SKLearn. Focused on model selection, cross-validation, and performance metrics (Precision, Recall, F1-Score) to solve multi-class and binary classification problems.
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Provider: Alura
Practical implementation of neural networks using the Keras high-level API. Focused on data preparation, building Sequential models, and executing training cycles for supervised learning tasks.
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Provider: Alura
Applied Keras for predictive modeling, focusing on hyperparameter tuning, model evaluation metrics, and optimizing neural networks for high-accuracy forecasting in production environments.
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Provider: Alura
Foundational implementation of neural networks. Covered data preprocessing for deep learning, building sequential models, and understanding activation functions in a supervised learning context.
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Provider: Alura
Comprehensive training covering the end-to-end lifecycle of deep learning models. Focused on tensor operations, building custom neural architectures, and implementing training loops using PyTorch for complex non-linear problems.
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Provider: Alura
I mastered the mechanics of deep model optimization by configuring advanced solvers like Adam and SGD, while strategically applying regularization methods such as Dropout and weight decay to ensure robust generalization and prevent overfitting during the backpropagation process.
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Provider: Alura
Focused on sequential data architectures, I utilized PyTorch to design and implement Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), effectively addressing the vanishing gradient problem to maintain temporal dependencies in complex time-series and natural language tasks.
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Provider: Alura
Advanced implementation of computer vision models. Focused on CNN architecture design, including convolutional layers, pooling, and dropout strategies for feature extraction and image classification tasks.
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Provider: Alura
This certification focused on the low-level implementation of neural architectures using Tensors and computational graphs, where I leveraged the torch.nn module to construct Multi-Layer Perceptrons and define custom weight initialization strategies for non-linear classification challenges.