Job description

• We are seeking a highly skilled Data Scientist with strong expertise in Deep Learning, Natural Language Processing (NLP), and Transfer Learning. The ideal candidate will be hands-on in building, optimizing, and deploying machine learning and neural network models using PyTorch and TensorFlow, with proven experience in working on BERT-based architectures, embeddings, and state-of-the-art NLP frameworks. Key Responsibilities • Experience Level: 6 12 Years • Design, develop, and deploy deep learning models for NLP, computer vision, or multimodal tasks. • Implement transformer architectures (e.g., BERT, GPT, RoBERTa) for various downstream applications such as text classification, sentiment analysis, summarization, and information retrieval. • Leverage transfer learning and embeddings to optimize model performance and scalability. • Research and experiment with cutting-edge deep learning methodologies, bringing innovations into production systems. • Collaborate with cross-functional teams (engineering, product, data engineering) to integrate models into production environments. • Perform data preprocessing, feature engineering, and model evaluation using industry best practices. • Optimize models for efficiency, scalability, and accuracy while addressing challenges such as class imbalance and large-scale data processing. • Stay updated with the latest advancements in AI, deep learning, and NLP research, applying them where relevant. • Proven experience (5+ years) in developing and deploying ML/DL solutions. • Strong hands-on expertise with PyTorch and TensorFlow. • Deep understanding of Neural Networks, Deep Learning, and Transformer-based models (BERT, GPT, etc.). • Solid knowledge of transfer learning, embeddings, and feature representation techniques. • Proficiency in Python and related libraries (NumPy, Pandas, Scikit-learn, Hugging Face Transformers). • Strong experience in model evaluation, hyperparameter tuning, and optimization. • Experience with cloud platforms (AWS, Google Cloud Platform, Azure) and MLOps tools is a plus. • Excellent problem-solving skills with the ability to work in fast-paced environments. • Experience with large language models (LLMs) and fine-tuning. • Exposure to multimodal learning (vision + NLP). • Knowledge of distributed training and optimization on GPTPUs. • Familiarity with vector databases (e.g., Pinecone, FAISS, Milvus) for embedding search. • Master s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or related field.

Requirements