ML Ai Engineer

The ML AI Engineer plays a critical role in implementing end-to-end AI solutions to support automation and data-driven decision-making at Green Earth Solutions. This position involves working closely with industrial cameras, barcode scanning systems, and real-time video processing pipelines, ensuring robust and efficient deployment of computer vision technologies on production lines. The engineer contributes to data preprocessing, model evaluation, and visualization tasks to improve accuracy and performance while adapting solutions to meet real-world constraints. Strong technical proficiency combined with a passion for creating cutting-edge AI applications makes this an exciting opportunity to advance your career in applied machine learning within a forward-thinking organization.
Responsibilities

Design, develop, and deploy machine learning models focusing on computer vision tasks such as object detection, counting, and classification on production lines.
Perform data preprocessing and augmentation to enhance model training, including handling industrial camera data and barcode scanning inputs.
Employ deep learning frameworks like TensorFlow or PyTorch to build and fine-tune models for embedded and edge computing environments.
Conduct thorough model evaluation using appropriate metrics to ensure high accuracy, robustness, and generalizability of AI solutions.
Collaborate closely with cross-functional teams to integrate AI models into real-time video analytics pipelines and industrial automation systems.
Implement efficient data visualization techniques to monitor model performance and provide actionable insights related to production quality and operational efficiency.
Contribute to continuous research and development efforts to incorporate state-of-the-art advancements in computer vision and machine learning.
Maintain thorough documentation of algorithms, codebases, and workflows to facilitate knowledge sharing and reproducibility.
Adapt AI solutions to the specific hardware and software constraints posed by edge devices and embedded systems.
Stay current with emerging trends and technologies in AI, machine learning, and computer vision, applying relevant innovations to improve existing systems.