Job Description

Responsibilities: 1. Design and Develop AI-Powered Products: Collaborate with cross-functional teams to design and develop AI-powered products that meet business requirements. Use foundational language models (e.g., BERT, RoBERTa, XLNet) and Transformer Architecture to build and train custom models for various applications. Experiment with different architectures, hyperparameters, and training techniques to optimize model performance. 2. Develop and Train Custom Models: o Develop and train custom models using large-scale datasets and state-of-theart techniques (e.g., transfer learning, few-shot learning). Fine-tune pre-trained language models for specific downstream tasks (e.g., sentiment analysis, question answering). Evaluate model performance using metrics such as accuracy, precision, recall, and F1-score. 3. Experimentation and Failure Analysis: Design and execute experiments to validate hypotheses about AI-powered product performance. Analyze results, identify areas for improvement, and propose changes to the product or solution. Collaborate with the team to implement design changes and iterate on the product development process. 4. Scaling Solutions: Develop and deploy scalable solutions that can handle large volumes of data and traffic. Optimize model performance for production environments using techniques such as model pruning, knowledge distillation, or quantization. Collaborate with infrastructure teams to ensure seamless deployment and maintenance of AI-powered products. 5. Knowledge Sharing and Collaboration: Share expertise and knowledge with colleagues through regular meetups, workshops, and blog posts. Participate in code reviews, provide feedback on peers' work, and contribute to open-source projects. Collaborate with other teams (e.g., data science, product management) to ensure alignment and effective communication. Technical Skills: o Strong foundation in deep learning, natural language processing, and computer vision. o Experience with foundational language models (e.g., BERT, RoBERTa, XLNet) and their applications. o Familiarity with popular deep learning frameworks (e.g., TensorFlow, PyTorch). o Knowledge of large-scale data storage and processing systems (e.g., Hadoop, Spark).