Job
Description
You will be responsible for designing, developing, and deploying deep learning models for various applications in Computer Vision and Natural Language Processing. Your role will involve working extensively with Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Transformer models. Conducting research on state-of-the-art deep learning algorithms and applying them to solve complex problems will be a key aspect of your job. You will be required to implement and fine-tune machine learning models using TensorFlow/PyTorch and collaborate with data scientists, researchers, and software engineers to integrate AI solutions into production environments. Optimizing deep learning models for scalability and efficiency, ensuring robust deployment in cloud or edge computing environments, will be among your responsibilities. Moreover, you will work on real-time and offline inference pipelines to deploy deep learning models efficiently. Staying updated with the latest advancements in Deep Learning, AI, and Machine Learning research and applying relevant insights to projects will be crucial. Your tasks will include performing data preprocessing, augmentation, and feature engineering to improve model performance. Developing custom loss functions, optimization techniques, and model architectures to enhance the efficiency of deep learning models will also be part of your role. Writing clean, efficient, and well-documented Python code for implementing and training machine learning models will be essential. In addition, you will conduct experiments, analyze results, and document findings for future reference and improvements. Working on improving model interpretability and explainability for real-world applications and collaborating with teams across different domains to understand business problems and design AI-driven solutions will be expected from you. The ideal candidate for this position should have a strong understanding of Deep Learning concepts and architectures. Expertise in Computer Vision (CNN, GAN) and NLP (RNN, LSTM, Transformer), proven experience with TensorFlow/PyTorch for training and deploying deep learning models, proficiency in Python, and experience in writing efficient and scalable machine learning code are necessary qualifications. Hands-on experience with model optimization, tuning, and hyperparameter search, solid understanding of mathematical concepts like linear algebra, probability, and optimization techniques, experience with handling large-scale datasets and data preprocessing techniques, familiarity with cloud platforms such as AWS, Azure, or Google Cloud for deploying deep learning models, strong problem-solving skills, and the ability to think critically in designing AI solutions are also required. Having a research background in Deep Learning is preferred but not mandatory. Experience with distributed computing and parallel processing frameworks is a plus, as well as familiarity with MLOps and Model Deployment Pipelines.,
