About Me
I'm Romain, a Machine Learning Engineer passionate about building AI systems that make a difference. I've worked with many kinds of data: tabular, text, vision, and time series, both professionally and in personal projects.
I bring expertise across the full spectrum of machine learning engineering and data science. My core skills include analyzing, cleaning, and augmenting data; creating visualizations to communicate insights to stakeholders; and developing predictive models. Building on this foundation, I specialize in managing the complete ML model lifecycle: training, evaluation, deployment, inference and continuous monitoring, through automated pipelines and CI/CD practices that ensure reliable, scalable solutions. Having led teams and delivered NLP solutions that processed hundreds of thousands of data points for business decision-making, I understand both the technical and collaborative aspects of successful ML projects.
During my academic research, I focused on improving internet accessibility for the blind and visually impaired through vision-enabled LLM agents. I continued this work by developing a version for the European Centre for Medium-Range Weather Forecasts (ECMWF).
I value human-centered projects and believe that work is not only about delivering results, but also enjoying the process and growing along the way.
Work Experience
Machine Learning Engineer & AI Product Owner @ Appox - Full Time
Montreal, Canada, Feb 2025 - Aug 2025, 7 months
As a Machine Learning Engineer at Appox, I led a team of four to develop NLP and ML solutions, helping domain experts analyze hundreds of thousands of customer feedback comments and build reports for business leaders. In this role, I've focused on enabling the complete ML model lifecycle (training, deployment, inference, continuous monitoring) through automated pipelines, and implementing CI/CD practices to ensure reliable, scalable solutions.
Data Scientist @ Appox - Full Time
Montreal, Canada, Aug 2024 - Jan 2025, 6 months
As a Data Scientist at Appox, I helped our clients meet their productivity challenges. To achieve this, I applied state of the art data science techniques such as domain understanding, meticulous exploratory data analysis, predictive and generative machine learning models, and efficient reporting. In a recent NLP project, our clients saw a 76% increase in productivity, helping save thousands of work hours over a year, using a solution I designed and implemented.
Machine Learning Researcher @ Shared Reality Lab - Part Time
Montreal, Canada, Sep 2022 - Jan 2024, 1 year 5 months
As a Machine Learning Researcher at the Shared Reality Lab (SRL), I led the development of an LLM-based assistant that helped nurses provide better care for dementia patients. This solution reduced administrative workload for healthcare staff while improving patient engagement through conversational AI.
During another project, I enhanced web accessibility for visually impaired users by developing computer vision models that extract and translate visual web content into accessible formats. This system now serves thousands of blind and low-vision users daily, significantly improving their ability to participate in online information exchange and decision-making processes.
Deep Learning Researcher @ Ecole Normale Supérieure - Internship
Paris, France, May - Aug 2022, 4 months
As a research intern at ENS, I worked on using deep learning to generate images from molecule formulas that could streamline the drug discovery process for cancer treatment. By adapting the DALL-E architecture, we were able to reduce the need for time-consuming and expensive wet-lab experiments.
Education
Master of Science in Computer Engineering @ McGill University
Montreal, Canada, 2022-2024
At McGill, I've had the opportunity to dive deep into the latest techniques in AI and machine learning. From developing transformer models for cancer treatment discovery to fine-tuning language models for disaster response, I've gotten hands-on experience applying AI to a wide range of domains. For my thesis, I'm working on using large language models to automatically summarize charts and graphs - a tool that could make data insights more accessible for the Blind or Low Vision.
Courses: Artificial Intelligence, Machine Learning, Deep Learning, Graph Neural Networks, Statistics, Human-Computer Interaction.
Master of Science in Computer Science @ ENSTA Paris
Paris, France, 2020-2022
My studies at ENSTA gave me a rock-solid foundation in machine learning, from the mathematical underpinnings to classic techniques like computer vision and reinforcement learning. Highlights included building a model to predict energy prices from real world indicators and developing an RL AI-agent that learns how to play a custom C++ game.
Courses: Artificial Intelligence, Machine Learning, Deep Learning, Feature Engineering, Reinforcement Learning, Statistics, Distributed Systems, Software Development, Cloud Computing, SQL, C++, Java, Git, Linux.
Bachelor of Science in Engineering
Paris, France, 2017-2020
My undergraduate studies in engineering laid a strong foundation in mathematics, physics, and computer science - all critical for my future work in AI. Courses in linear algebra, probability, statistics, algorithms, and data structures equipped me with the analytical thinking and problem-solving skills needed to tackle complex challenges in machine learning and data science.
Projects
I'm always tinkering on side projects to learn new skills and try out ideas. If you want to know more about them, feel free to check out some of my blog posts.