AI ML Engineer

• Continuous Research & Paper Reviews – Keep up with the latest advancements by reviewing academic research, integrating state-of-the-art methodologies, and refining AI models for higher accuracy and efficiency.
• Computer Vision & Image Analysis – Process and enhance visual data to identify, classify, and extract relevant features from plant images.
• AI Model Optimization for Classification & Extraction – Develop and optimize AI models to detect and classify plant species and extract insights.
• MLOps for Automation & Optimization – Streamline AI workflows by enhancing visualization tools, evaluation pipelines, and training pipelines, ensuring efficient model deployment for edge robots and annotation tasks.
• Comprehensive Documentation & Documented Code – Ensure all AI models, algorithms, and processes are well-documented for transparency, reproducibility, and seamless collaboration. This includes structured technical reports, inline code documentation, and process flow descriptions.
• Scalable Code & Collaboration – Maintain optimized, peer-reviewed code to support teamwork, innovation, and long-term maintainability across AI development teams.

Your profile :

• Ph.D. or Master's degree in Computer Science, Robotics, Electrical Engineering, or a related field with strong orientation in Machine Learning.
• Proficiency in English and French, both spoken and written.

You have :

• Industry & Research Background – Minimum 3 years of industry experience or 5+ years in applied research and development within an academic institution.
• Programming Expertise – Proficiency in Python, with additional experience in other languages (C++, CUDA, and others) appreciated. Familiarity with Docker and Ubuntu, along with an understanding of CI/CD pipelines, would be highly valued.
• Computer Vision & Image Processing – Strong knowledge of OpenCV and related tools for visual data processing.
• Machine Learning & AI Frameworks – Hands-on experience with PyTorch, JAX, or TensorFlow for AI model development and optimization.

You are familiar with :

• MLOps & Automation Tools – Model training pipelines, evaluation workflows, and visualization tools to streamline AI development.
• Dataframe Manipulation & Selection – At ease in handling large datasets using Pandas, NumPy, and other data processing libraries for efficient data selection, filtering, and preprocessing.
• Deployment & Optimization – TensorRT or other AI deployment frameworks for efficient model inference on edge devices.
• Proactive & Autonomous – Demonstrates strong initiative and the ability to work independently.
• Team-Oriented & Adaptable – Thrives in fast-paced environments and collaborates effectively with diverse teams.
• Passionate & Curious – Eager to take on new challenges, even beyond core expertise.
• Analytical & Agile – Approaches work with a structured mindset while quickly adapting to new inputs and evolving requirements.
• Environmentally Conscious – Committed to sustainability and green technology initiatives.