Principal Applied Scientist

Optimize model performance, scalability, and efficiency Conduct experiments to evaluate model performance, robustness, and generalization Implement customization techniques for various NN based architectures Explore novel techniques and approaches to enhance model capabilities Stay up-to-date with the latest advancements in NLP, deep learning, and AI research Work with large-scale datasets, preprocess them, and create appropriate representations Select relevant features and ensure data quality for training and evaluation Develop and deploy customized LLM solutions for customer scenarios Optimize models using fine-tuning, distillation, and synthetic data generation Mentor and guide team members to foster innovation and technical excellence Build novel data generation solutions to synthesize complex speech scenarios and finetune models. Build data analysis metrics and solutions to understand the modelresults, identify gaps, and guide solutions. Mentor and influence peers, sharing expertise and fostering a growth-oriented inclusive team culture. 10+ years of experience in machine learning, with a strong focus on GenAI and LLMs Depth in Data Science,Generative AI and Engineering Ph.D. or Master's in CS, AI, or a related field Hands-on experience with LLM fine-tuning, model compression, and synthetic data generation preferred strong background in machine learning, deep learning, and natural language processing Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch) Experience with transformerbased models (e.g., BERT, GPT, T5, Llama) Familiarity with cloud platforms (e.g., Azure, AWS) and distributed computing Solid understanding of statistics, linear algebra, and probability theory is preferred Excellent problem-solving skills and the ability to work independently and collaboratively Proven ability to build, optimize, and scale AI models in production.