AI Engineer - Computer Vision at Detect in Miami

Summary: Detect is a fast-growing company focused on turning complex data into actionable insights through innovative technology. They are seeking an AI Engineer to contribute to the development and evaluation of computer vision models for their AI-driven inspection platform, working on tasks such as object detection and image understanding. Responsibilities: - Experiment with and prototype computer vision models: Design, train, and evaluate deep learning models for object detection, segmentation and classification on real-world infrastructure imagery - Support MLOps and data pipelines: Collaborate with the team to improve data preprocessing pipelines, model evaluation tools, and ML lifecycle tracking systems using tools like MLflow - Perform error analysis and quality improvements: Analyze failure modes in models and datasets, and contribute to strategies for improving performance across edge cases Required Qualifications: - Experience building and deploying machine learning or computer vision solutions in real-world environments (production systems, customer-facing products, or internal platforms). Demonstrated experience through internships, research, or substantial projects is acceptable - Strong foundation in computer vision and deep learning, with hands-on experience in areas such as object detection, semantic segmentation, or image classification - Proficiency in Python and practical experience with deep learning frameworks such as Pytorch or Tensorflow - Experience working with end-to-end ML workflows, including data preprocessing, model training, evaluation, and iteration - Comfortable working in ambiguous problem spaces and translating real-world constraints into technical solutions - Strong communication skills and the ability to collaborate effectively with engineers, product, and operational teams Preferred Qualifications: - Experience deploying or supporting ML models in production (e.g., APIs, batch inference pipelines, edge deployment, or cloud-based systems) - Hands-on experience with MLOps tooling such as MLflow, Weights & Biases, DVC, or similar experiment tracking and lifecycle tools - Familiarity with cloud platforms (GCP, AWS, or Azure) and containerized workflows (Docker) - Experience with large-scale or high-resolution imagery, including aerial, satellite, or infrastructure inspection data - Experience improving model performance through data-centric approaches (dataset curation, labeling strategies, augmentation) Required Skills: Computer Vision, Deep Learning, Object Detection, Semantic Segmentation, Image Classification, Python, PyTorch, TensorFlow, Machine Learning Workflows, MLOps, MLflow