ML Engineer I

Data Science 1. Deep Learning • Strong understanding of neural networks, CNNs, transfer learning, loss functions and optimization. • Hands-on expertise with PyTorch and/or TensorFlow/Keras. • Experience developing and debugging end-to-end training and inference pipelines. 2. Computer Vision & Image Processing • Strong hands-on experience with OpenCV. • Image filtering, enhancement and transformation. • Edge/contour detection. • Feature extraction and keypoint matching. • Image segmentation. • Object detection and tracking. 3. AI / Machine Learning • Strong ML fundamentals including supervised/unsupervised learning, feature engineering, model evaluation and optimization. • Experience with Scikit-learn, NumPy, Pandas and SciPy. • Ability to select appropriate algorithms and architectures based on business and engineering problems. 4. Programming • Advanced Python programming. • Strong OOP, debugging, optimization and automation skills. • C++ exposure would be advantageous, particularly for performance-intensive vision solutions.