Deep Learning (DL) Engineer

Overview

We are seeking a highly skilled Deep Learning Engineer with strong expertise in Python, frameworks like TensorFlow, Keras, and PyTorch, and a solid understanding of Neural Networks. The ideal candidate will have experience developing and deploying Deep Learning models, optimizing them using CUDA for GPU acceleration, and applying their knowledge to solve complex challenges in Data Science. This role offers an opportunity to work on cutting-edge projects in AI and ML, pushing the boundaries of innovation.

Key Responsibilities

• Model Development: Design, implement, and train deep learning models using TensorFlow, Keras, and PyTorch for various applications.
• Optimization: Utilize CUDA and other GPU-accelerated techniques to optimize model training and inference.
• Neural Network Architectures: Develop and experiment with advanced architectures, including CNNs, RNNs, GANs, and transformers.
• Data Preparation: Preprocess and augment large datasets for effective model training and validation.
• Evaluation: Assess model performance using appropriate metrics, fine-tuning for optimal results.
• Deployment: Deploy deep learning models into production environments with a focus on scalability and efficiency.
• Collaboration: Work closely with data scientists, software engineers, and product teams to integrate deep learning solutions into applications.
• Research: Stay updated with the latest advancements in Deep Learning and contribute innovative ideas to projects.
• Documentation: Maintain detailed records of experiments, model designs, and implementation processes for reproducibility.

Key Requirements

• Technical Skills:
• Proficiency in Python and libraries like NumPy and Pandas.
• Expertise in deep learning frameworks: TensorFlow, Keras, and PyTorch.
• Experience with GPU acceleration and CUDA programming.
• Strong knowledge of Neural Network architectures and their applications.
• Familiarity with optimization techniques and hyperparameter tuning.
• Mathematical Background: Strong understanding of linear algebra, calculus, and probability.
• Problem-Solving: Ability to address complex challenges with innovative deep learning solutions.
• Experience: Hands-on experience in deploying and optimizing deep learning models in real-world scenarios.
• Teamwork: Strong collaboration skills and experience working in cross-functional teams.