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:Experience4+ 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 SkillsStrong 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 SkillsStrong 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.EducationBachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related technical field.Preferred QualificationsExperience 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.