Freelancer profile translated to English.
Back to original languageAbout Maxime
👋 Hello,
I am passionate about creating innovative solutions in **Machine Learning**, **Deep Learning**, and **Ops**, transforming algorithmic abstraction into tangible applications.
I frequently consult articles, which demonstrates my keen interest in research and development.
✨ My specialty? Realizing the potential of AI.
**Machine Learning**:
- **Regression**: Linear Regression, Ridge, Lasso.
- **Classification**: Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forests, Gradient Boosting.
- **Clustering**: K-Means, Hierarchical Clustering, DBSCAN.
- **Dimension Reduction**: PCA, t-SNE, UMAP.
- **Model Selection**: Cross-validation, Grid Search, Random Search.
- **Data Preprocessing**: Imputation, Normalization, Standardization.
**Deep Learning**:
- **Neural Networks**: CNN, RNN
- **Network Architecture**: Deep network design
- **Natural Language Processing (NLP)**: Transformers, LLMs
- **Machine Translation**: Speech-to-text, Image-to-text, Video-captioning
- **Computer Vision**: Object detection, Image classification, Object tracking, Facial recognition.
**Ops**:
- **Deployment**: Putting models into production
- **Cloud**: Deploying solutions in the cloud environment
- **Containerization**: Docker, Kubernetes for orchestration
- **CI/CD**: Continuous Integration and Deployment
- **Vector Search**: ElasticSearch, OpenSearch for large-scale data analysis
- **Monitoring**: Monitoring and logging with Prometheus, Grafana
**AWS Specific**:
- **Search**: OpenSearch
- **Messaging**: SQS queue
- **Container Orchestration**: ECS, Fargate
- **Data Architecture**: Pipeline design, Step Functions
Still here? Feel free to check out my **portfolio**!
French
Native or bilingual
English
Fluent
Can work on-site
Paris (up to 50km), Bordeaux (up to 50km), Lyon (up to 50km), Strasbourg (up to 50km)
Experience
- Canal+Machine Learning EngineerFILM AND AVSeptember 2023 - Today (2 years and 9 months)Paris, FranceDevelopment of a scalable pipeline using a VLM for the "Et ta Scène?" feature, reducing costs by 90%- Design of the "Et ta Pub?" system for automated chapter extraction and ad placement optimization using LLMs- Creation of "Et ta Marque?" for brand recognition in movies/series using LLM technology- Design of "Et ton Thème?" for intelligent content chaptering with subject, context, and viewpoint analysis- Implementation of metrics to evaluate content diversity and plurality- Automation of workflows via AWS Step Functions and Lambda- Development of generic workers for in-depth analysis and understanding of global content
- JakartoComputer Vision EngineerAUTOMOBILEJune 2023 - August 2023 (3 months)Montréal, CanadaClustering approach for traffic signs using deep learning:- Development of an optimization module to improve object separation within clusters, thereby enhancing the accuracy of object classification in urban inventory.- Utilization of advanced data capture systems, including LiDAR scanners, cameras, and high-precision GPS for triangulation and geographic positioning.- Use of a Siamese network to train an encoder that facilitates object separation by the clustering algorithm.- Conducted comprehensive experiments using Fashion MNIST and traffic sign datasets to evaluate the performance of the developed module.- Achieved a v-measure of 0.94, demonstrating the high efficiency of the developed process for differentiating objects within a cluster.- Contributed to improving the efficiency of urban inventory by enabling more accurate classification of detected objects.
- Hache de LancerComputer Vision EngineerSPORTSMay 2023 - August 2023 (3 months)Paris, FranceDesign of vinyl cover recognition for an application:- R&D feature engineering for recognition (200 ms request per end-to-end inference)- Identification of the nearest vector in a large dataset within the FAISS index (millions of vinyls)- GitHub CI/CD and deployment on Scaleway- GitHub Actions
Reviews
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Education
- AI and Big Data EngineerTELECOM NancyUne formation « généraliste » développée à l’école au travers de cinq axes : trois axes couvrant le « cycle de vie d’un produit, logiciel ou service informatique » et deux axes transverses, couvrant les sciences fondamentales et appliquées ainsi que celles du management, de la communication et de l’innovation. L'approfondissement « Intelligence Artificielle et masses de données » est développé en entreprise (LogIC SAS).
- CPGE TSILycée Louis VincentClasse préparatoire aux grandes écoles en technologies et sciences industrielles. Approfondissement en Mathématiques, Physique et Science de l'ingénieur.