IT Consultant | Software Developper | Cloud & Data Integration | Data Engineering | ETL | AI Solutions Specialist

10/2022 – 10/2023

- Conducted comprehensive market analysis of the AWS Marketplace to identify opportunities for Natural Language Processing (NLP) and Optical Character Recognition (OCR) solutions tailored to the automotive industry.
- Spearheaded a team of developers to fine-tune AI models, optimizing parameters specifically for automotive datasets, ensuring high accuracy and performance.
- Developed and deployed two premium AI-driven solutions for a global automotive clientele, achieving a 30% increase in performance compared to baseline models.
- Collaborated with Solutions Architects and Sales teams to align technical strategies with business goals, ensuring seamless integration and adoption of the solutions by clients.
- Communicated deployment strategies and benefits to stakeholders, securing ongoing collaboration and trust while expanding engagement opportunities in the AWS ecosystem.

Amazon Web Services (AWS), Computer Vision, Natural Language Processing, Python

1/2022 – 9/2022

• Pitched project advancements and PoCs to academic and industry stakeholders, leading cross-functional meetings to align technical outcomes with business requirements and secure buy-in for further development.
• Developed a novel system that combined multiple data sources and languages to improve the representation of social media content, achieving more accurate and comprehensive insights than previous methods.
• Automated data labeling processes and extracted features using state-of-the-art NLP models and computer vision models, reducing manual labeling time by 40% and accelerating preprocessing workflows for large-scale datasets.
• Engineered neural network architectures and supervised the training to address a classification task, achieving an accuracy improvement of 20% over baseline models.
• Evaluated the learned embedding of the trained models using unsupervised machine learning methods, resulting in a 20% increase in clustering performance.

Data Science, Natural Language Processing, Python, Pytorch

4/2020 – 7/2021

• Designed a data analysis pipeline for bacterial genome research, enabling the manipulation of gene expressions for over 100 doctoral students and integrated ETL workflows and statistical analysis tools to automate data processing, reducing manual effort by 40% and accelerating insights.
• Developed and led the implementation of the ”Gaussian Hellinger Very Fast Tree” (GHVFDT) for detecting pulsar candidates, processing over 100,000 records in an unbalanced data stream, and conducted 50+ experiments to evaluate model performance, resulting in a 15% increase in detection accuracy over benchmark models.
• Preprocessed and fine-tuned large-scale internet crawl data to improve a Large Language Model (LLM) for sentiment analysis, increasing accuracy by 20%, and deployed the model into a web application for real-time user insights and decision-making.

Data Science, Data Mining, ETL, SQL, Large Language Models, Pytorch, Tensorflow, Python