Deep Learning Engineer

Key Responsibilities

• Design, train, and optimize deep learning models for various applications such as computer vision, NLP, speech recognition, and reinforcement learning.
• Implement state-of-the-art architectures, including CNNs, RNNs, Transformers, GANs, and LSTMs.
• Work with large-scale datasets for preprocessing, augmentation, and feature extraction.
• Develop and deploy deep learning models in production environments.
• Optimize models for speed, accuracy, and efficiency using quantization, pruning, and distributed training.
• Utilize GPU acceleration (CUDA, TensorRT) and cloud computing (AWS, GCP, Azure) for model training and inference.
• Collaborate with cross-functional teams to integrate AI solutions into products.
• Stay up to date with the latest deep learning research, frameworks, and technologies.

Required Qualifications

• Bachelors/Masters/Ph.D. in Computer Science, AI, Machine Learning, Data Science, or related field.
• 2+ years of experience in deep learning, neural networks, or AI research.
• Strong proficiency in Python and frameworks like TensorFlow, PyTorch, Keras.
• Solid understanding of mathematical foundations behind deep learning (linear algebra, probability, optimization).
• Experience with cloud platforms (AWS, GCP, Azure) and MLOps practices.
• Hands-on experience with training, fine-tuning, and deploying deep learning models.
• Strong knowledge of data preprocessing, augmentation, and feature engineering.
• Familiarity with distributed computing and parallel processing techniques.

Preferred Skills

• Experience with LLMs (Large Language Models), multi modal AI, or self-supervised learning.
• Knowledge of edge AI, federated learning, or on-device ML.
• Experience with containerization (Docker, Kubernetes) and micro services for ML deployment.
• Strong problem-solving, critical thinking, and research abilities.