Machine Leaning Engineer - Computer Vision

Main tasks and responsibilities

• Develop computer vision and deep learning applications related to object detection, object segmentation and activity/action detection.
• Scientific thinking and the ability to invent, implement, and lead technology developments in the field of computer vision and machine learning.
• Dedicated to delivering Machine Learning projects.
• Utilizing existing hardware and images in addition to new image data gathering techniques to produce innovative image analysis models and algorithms.
• Lead the ideation, prototyping, and development of AI software.
• Demonstrate expertise in solving computer vision problems.
• Develop deep learning and traditional machine learning algorithms.
• Design and develop scalable software architectures.
• Demonstrate ongoing understanding of Machine Learning technologies in current marketplace and how they can be applied to the business.
• Facilitate design and deployment of vision hardware equipment needed for image data gathering.
• Create and maintain data pipeline architecture for ML algorithm development.

Requirements

• MS or PhD in Computer Science, Engineering, Mathematics or Statistics, with specialization in computer vision and deep learning.
• Minimum 2 years of industrial experience in developing and deploying Computer Vision and Deep Learning applications in Production at scale.
• Experience in software development, integration with deep learning algorithms and deployment in production.
• Proficiency in scientific understanding and implementation of deep learning architectures in Computer vision (Image classification, object detection, segmentation, pose estimation) from ideation to productionized deployment.
• Software development in Python, Deep learning (Tensorflow and Pytorch), Machine Learning libraries (OpenCV, Scikit-learn, NumPy, Pandas) and Data Analytics/Reporting.
• DevOps: Code Management, Version Control, Code Review, CI/CD, Configuration Management, Monitoring, Containerization
• MLOps: Experiment Management (Model Versioning, Parameter Versioning, Model Performance Metrics), Model Integration, Serving, Deployment, Testing and Continuous Monitoring
• DataOps: Data Gathering, Annotation, Quality, Visualization, Versioning, and Engineering.
• Ability to read scientific publications, understand and implement proposed solution.
• Excellent written and spoken communication skills.
• Self-driven and strong problem-solving skills.
• Team work.
• Strong analytical skills and process focus