About Me:
💡 I am an accomplished AI ML Engineer with a robust background in software engineering, I have honed my skills to specialize in machine learning over the past years.
🎓 In my continuous pursuit of knowledge and skills advancement, I successfully completed a second Master's degree in Data Science and Artificial Intelligence at Mines Paris in collaboration with Data Science Test. This academic journey not only enhanced my capabilities as an AI ML Engineer but also equipped me with comprehensive skills to excel as a ML engineer and LLMOps / LMOps specialist.
🔬 As an AI ML Engineer, I have a proven track record of developing and deploying machine learning models that drive actionable insights and enable data-driven decision-making. I am experienced in the end-to-end development of ML systems, from data preprocessing and feature engineering to model selection, training, and deployment. I am well-versed in various ML algorithms and frameworks, including TensorFlow, PyTorch, and scikit-learn.
💻 In addition to my expertise in AI and ML, I bring a wealth of knowledge in software engineering and mobile.
🌟 With strong problem-solving and analytical skills, I thrive in challenging and dynamic environments. I am highly collaborative, possessing excellent communication skills and the ability to work effectively in cross-functional teams.
✉️ Get in touch on LinkedIn & Stack Overflow
🌎 I am fluent in English and French, and I have experience working in international settings in France, Spain, Canada, the United States, and Switzerland. 🇫🇷🇨🇦🇺🇸🇪🇸🇨🇭
📂 My Projects:
Machine Learning (ML):
- 🧠 Brain MGMT Prediction: Comprehensive analysis and model development for MGMT prediction using an XGBoost ensemble with a U-Net-based feature extractor. | Kaggle |
Deep Neural Networks (DNN):
- 🍷 Wine Quality Prediction: Utilized DNN to predict wine quality based on physicochemical properties. | Kaggle |
Computer Vision (CV):
- 🧠 Brain MGMT Prediction: Developed a CNN-based model for prediction of the MGMT. | Kaggle |
- 🌾 Rice Grain Classification: Employed CNN to classify different types of rice grains. | Kaggle |
- 🚦 Traffic Sign Recognition (GTSRB): Developed a CNN-based model for real-time traffic sign recognition. | Kaggle |
- ✍️ Handwritten Digit Classification (MNIST): Achieved high accuracy in classifying handwritten digits using CNN. | Kaggle |
Generative Adversarial Network (GAN)
- 🚲 Bicycle Image Generation: Exploration of Generative Models (GAN) and its capabilities, focusing on the generation of bicycle. | Kaggle |
- Base Stemming and Lemmatization
- Base Stemming Lemmatization Classification
- Base TF-IDF Similarity
- Base Neural Word Embeddings
- Weaviate VectorDB
- Weaviate Nearest Neighbors
- Weaviate CRUD Search
- Weaviate Sparse Dense Hybrid Search
- Weaviate Multilingual Search
- Weaviate Search
- Weaviate Dense Retrieval
- Weaviate ReRank
- RAG LLamaIndex Pipeline
- RAG LLamaIndex Sentence Window Retrieval
- RAG LLamaIndex Auto Merging Retrieval
- RAG LLamaIndex Ollama Auto Merging Retrieval
- RAG Agent