Artificial Intelligence Engineer

Key Responsibilities: • Design, develop, and deploy computer vision and machine learning solutions across image and video analytics use cases, including object detection, segmentation, classification, and OCR. • Evaluate and benchmark modeling approaches, balancing accuracy, performance, scalability, and business requirements. • Train, fine-tune, and optimize deep learning models using frameworks such as PyTorch or TensorFlow, leveraging transfer learning and other efficient training methods. • Define success metrics, analyze model results, troubleshoot performance issues, and continuously improve model effectiveness. • Incorporate real-world considerations such as camera hardware, sensors, lighting conditions, and edge computing constraints into model development. • Develop and maintain data ingestion, annotation, training, and evaluation pipelines to support experimentation and production deployment. • Partner with software engineering, data engineering, and MLOps teams to operationalize machine learning solutions and accelerate time to value. Required Qualifications: Experience • 4+ years of hands-on experience developing and deploying computer vision and machine learning solutions. • Demonstrated experience taking models from research and prototyping through production implementation. Technical Skills • Strong understanding of modern deep learning techniques, including CNNs, transformers, embeddings, and traditional computer vision methodologies. • Advanced Python programming skills for machine learning, computer vision, and data processing. • Experience with PyTorch and/or TensorFlow. • Hands-on experience with image and video analytics, including detection, segmentation, classification, and OCR. • Working knowledge of imaging hardware, sensors, lighting environments, and edge computing considerations. • Experience applying machine learning techniques such as anomaly detection, clustering, forecasting, or predictive modeling. Professional Skills • Strong analytical, troubleshooting, and model performance optimization capabilities. • Ability to translate business objectives into practical AI and machine learning solutions. • Excellent problem-solving, communication, and collaboration skills in an agile environment. Education • Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related technical field. Preferred Qualifications • Experience deploying and optimizing models for edge devices using techniques such as quantization, pruning, TensorRT, or ONNX. • Familiarity with MLOps practices, including experiment tracking, model versioning, monitoring, and lifecycle management. • Experience with cloud-based machine learning environments in Azure, AWS, or Google Cloud Platform. • Experience working with large-scale image, video, IoT, or sensor-based datasets.