About Me

I am currently an AI Scientist at Vooban, specializing in applied AI consulting. My role involves end-to-end delivery of AI solutions—from feasibility studies and solution design to development and production deployment. I work across a variety of domains including computer vision, audio processing, LLMs/VLMs, NLP, generative AI, and recommender systems, with a strong emphasis on building client-ready products.
I also have a M.Sc. degree in Robotics and Computer Vision from Polytechnique Montreal. My experience and technical interests include robotics, controls, computer vision, artificial intelligence, autonomous vehicles, and AI Ethics (I am also a Space nerd!).

With a strong mathematical and programming background, my main technical skills include Python (OpenCV, scikit-learn, NumPy, pandas, pydantic, Hugging Face, etc.), deep learning with PyTorch and TensorFlow, LLMs/VLMs and prompt engineering, C/C++, Matlab/Simulink, Docker, MLOps tools, as well as Robot Operating System (ROS).

I have had a chance to participate in multiple projects since I started studying engineering, many of them using Agile methods. I'm a dedicated and conscientious engineer, with strong leadership, teamwork, and technical communication skills, and a profound desire to learn and improve.

Outside of engineering, I also enjoy doing multiple things: I play volleyball, do calisthenics and bouldering multiple times a week, I enjoy the outdoors—hiking and scuba diving—I play drums and chess in my offtime, and I particularly enjoy movies and books (Ask me about my favorites!).

Completed projects(Click to see details)

See All Projects

I also have a M.Sc. degree in Robotics and Computer Vision from Polytechnique Montreal. My experience and technical interests include robotics, controls, computer vision, artificial intelligence, autonomous vehicles, and AI Ethics (I am also a Space nerd!).

Work Experience

AI Scientist 2023-Present

AI Scientist at Vooban, specializing in applied AI consulting. Responsible for end-to-end delivery of AI solutions. Includes feasibility studies, solution design, development and production deployment. Areas of work cover computer vision, audio processing, LLM/VLMs, NLP, generative AI, and recommender systems, with a strong emphasis on building client-ready products.

Key Contributions

  • AI development: Built and deployed models for object detection/segmentation, text-voice alignment, voice isolation, LLMs/VLMs, OCR, and recommender systems.
  • Full-stack AI pipelines: Designed workflows covering data collection, annotation, preprocessing, training, evaluation, and deployment.
  • Architecture & planning: Defined project architectures, estimated resources (Jira), and led feasibility assessments with clients.
  • Production integration: Integrated research prototypes into production codebases, wrote documentation and technical guides.
  • Training & outreach: Delivered workshops and conferences on Ethical & Responsible AI for diverse audiences (entrepreneurs, developers, marketing teams).

Example projects

  • Detection assistance for dubbing projects: Using a mix of audio processing, voice recognition, computer vision and traditional phoneme theory, detect several relevant elements of movies to assist with automated dubbing workflows, reducing manual editing time by 50%.
  • Automatic takeoff of steel structures from architectural drawings: Using computer vision and OCR techniques to automatically extract measurements and quantities from blueprints.
  • Donator probability estimation: Using traditional machine learning and statistical models to predict donation likelihood from client data.
  • Automatic countertop submission form extraction: Using computer vision, OCR and form parsing techniques to automatically extract data from submission forms, accelerating data entry.
  • Automatic X-Ray auto part defect detection: Using object detection models to identify defects in automotive parts from X-ray images. Focusing on optimizing recall to reduce false negatives in quality control.

Tools & Technologies
AI-related: Python, PyTorch, Hugging Face, scikit-learn, OpenCV, pydantic-ai, LLM/VLM, Roboflow, MLFlow, Windsurf & other coding assistants, prompt engineering.
General programming: Git, Docker, MacOS.
Generic skills: project scoping, client communication, technical writing, workshop facilitation, multi-disciplinary collaboration, agile development, version control, re-inventing programming with agents in the loop.

Software Developer at the Canadian Space Agency (CSA) through the Engineering Development Program. Spear-headed the agency's AI initiative—defining strategic priorities, recommending hardware, and proposing exploration projects to grow AI expertise across the organization. Worked at the intersection of robotics, deep learning, and systems engineering, with a focus on adapting modern AI techniques to the unique constraints of space environments.

Key Projects

  • Rover autonomy: Developed vision and path-planning algorithms for test rovers under strict compute budgets using ROS.
  • Radiation resilience: Investigated how space phenomena (radiation, bit flips) degrade neural network performance and explored mitigation strategies.
  • Low-light imaging: Prototyped deep-learning pipelines to correct extreme low-light satellite and rover imagery.
  • ISS science reviews: Provided technical oversight for science payloads destined for the International Space Station.
  • Hardware test automation: Built Python tools (GUI + protocols) to streamline stress testing of electrical components.
  • Satellite data processing: Acquired and processed high-resolution imagery and time-series data from multiple satellite sources.
  • Contributed to the recruitment process by reviewing applications, creating interview questions, and participating in technical interviews for the next cohort of engineers.
  • Communication & leadership: Built introduction presentations on Object-Oriented Programming and Deep Learning for colleagues without programming backgrounds. Supervised university students on space related projects.

Tools & Technologies
AI-related: Python, PyTorch, OpenCV, ROS, Gazebo, Multi-threading.
General programming: Git, Linux, Docker, C++.
Generic skills: systems engineering, technical writing, literature review, project management, communication & presentation (both technical and general public).

Research Scientist at Nuance Communications (2020-2022). My role was to contribute to the improvement of Nuance's NLP solutions, particularly for chatbots. My main contributions included optimizing parameters to improve performance, as well as helping to maintain and update codebases.

In particular, most of my work focused on improving model performance on very small training sets without altering the model architecture. I explored techniques such as simple data augmentation, complex data augmentation using generative models, Adversarial Training, and Few-shot Learning.

Tools & Technologies
AI-related: Python, TensorFlow, Hugging Face Transformers, BERT & Transformers.
General programming: Git, Java, Docker.
Generic skills: code review, cross-functional collaboration, technical writing & documentation.

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Education

After graduating in E.E., I decided to specialize in robotics with a professor I had been working with for over 3 years, Prof. Sofiane Achiche.

MY focus was on artificial intelligence, computer vision, and control systems. GPA: 4.00/4.00



My project was to develop an autonomous feeding system for people with disabilities using the MICO arm by Kinova. The main tasks consisted of:

  • Implementing a food detection algorithm with Tensorflow & Python to classify and locate food in the 3D workspace. Combines Faster RCNN with Intel stereo camera for depth estimation.
  • Setup-ing a ROS environment for simulation and control. This environment is now the baseline used in the lab.
  • Path-planning & Obstacle Avoidance of a 6DoF robot arm. Implemented using MoveIt! and OMPL libraries, as well as octomaps for 3D environment mapping.
  • Developping an intuitive and robust way of sending commands for people with limited movement

As an Electrical Engineering undergraduate student at Polytechnique Montreal, Canada, I had the opportunity to learn about the following fields:

  • Controls (stability, full-state feedback controllers and observers, discretization, real-time systems)
  • Signal Processing
  • Artificial intelligence and computer vision(classical methods and deep learning)
  • Advanced calculus, linear algebra and probability

I graduated with honors with a 3.84/4.00 GPA and multiple awards.

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Skills

Some of my most significant skills to help in engineering tasks.

PyTorch/Tensorflow

90%

Python(numpy, pandas, sklearn, pydantic, etc.)

95%

Object-Oriented Programming & Programming Principles

85%

C/C++

65%

OpenCV & Traditional Computer Vision

85%

Git & Programming Tools

90%

Robotics, controls & dynamics

90%

Robot Operating System (ROS)

80%

LLMs / VLMs & Prompt Engineering

85%

Matlab/Simulink

90%

Docker & MLOps

75%

Cooking Risotto

110%

Technical Communication

95%

Playing Chess

90%

Contact

If you would like to get in touch for any question, inquiry, comment, or to connect and discuss, feel free to email me at descoteaux.gabriel@protonmail.com. You can also reach me on LinkedIn.