Machine Learning Engineer

Deep Learning / ML Engineer Key Responsibilities: • Design, develop, and implement deep learning models using architectures such as CNNs, RNNs, and GANs, with a focus on state-of-the-art computer vision, image processing, and signal processing techniques. • Apply machine learning algorithms for feature extraction, classification, detection, and prediction from complex image and signal data. • Utilize advanced image and signal processing methods to enhance model performance, data quality, and system robustness. • Optimize machine learning models for deployment on edge devices, ensuring efficient use of limited computational resources. • Collaborate with cross-functional teams to integrate ML solutions into production systems. • Write clean, efficient, and well-documented Python code following best software development practices. Required Qualifications: • 2–5 years of hands-on experience developing and optimizing machine learning models and algorithms, with an emphasis on computer vision, image processing, and signal processing. • Strong proficiency in Python; experience with libraries such as TensorFlow, PyTorch, NumPy, SciPy, and OpenCV is highly desirable. • Solid understanding of software development practices including version control (e.g., Git), testing, and continuous integration. • Proven experience with performance optimization techniques for ML models, especially in edge-computing environments. • Strong background in machine learning and deep learning, particularly in signal and image processing applications. Education: • Ph.D. degree in Computer Science, Robotics, Electrical Engineering, Computer Engineering, Mathematics, or a related field with a focus on deep learning, computer vision, or signal/image processing. Soft Skills: • Strong analytical and problem-solving skills with a keen attention to detail. • Excellent communication and collaboration skills to work effectively with interdisciplinary teams and stakeholders. • Highly organized, self-motivated, and capable of managing multiple priorities in a fast-paced environment.