LLMs Specialist

Overview

We are seeking a skilled LLMs Specialist to join our team. The ideal candidate will have expertise in large language models (LLMs), sentiment analysis, and custom model training. You will work with various architectures such as transformers and LLaMA, and be involved in training, fine-tuning, and evaluating models for various applications. This role requires strong analytical skills, experience with sentiment analysis, and knowledge of both online and offline LLM solutions.

Responsibilities

• Design, develop, and fine-tune large language models (LLMs) for various applications including sentiment analysis. Work with architectures such as transformers, LLaMA, and other custom models.
• Implement and optimize sentiment analysis algorithms to extract insights from text data. Develop models to accurately classify sentiment and improve performance over time.
• Create and manage training pipelines for custom LLMs. Utilize frameworks like Hugging Face for model development and training. Fine-tune pre-trained models to meet specific needs.
• Develop and deploy offline LLM solutions to ensure functionality in environments with limited internet access. Optimize models for performance in offline scenarios.
• Assess model performance using metrics such as accuracy, precision, recall, and F1 score. Conduct thorough evaluations and implement improvements based on testing results.
• Prepare and preprocess datasets for training, including data cleaning, augmentation, and management. Ensure high-quality data for effective model training and evaluation.
• Work closely with cross-functional teams to integrate models into applications and products. Provide insights and recommendations based on model performance and data analysis.
• Maintain comprehensive documentation of model architectures, training processes, and performance metrics. Share knowledge and best practices with the team.

Requirements

• Proven experience with large language models (LLMs) and sentiment analysis. Familiarity with transformer architectures and custom model training.
• Proficiency in machine learning frameworks and libraries, such as Hugging Face, TensorFlow, PyTorch, and Scikit-Learn.
• Strong understanding of different LLM architectures, including transformers and LLaMA. Experience with offline LLM solutions.
• Ability to analyze and interpret complex data, with experience in model evaluation and performance optimization.
• Proficiency in programming languages such as Python, with experience in developing and deploying machine learning models.
• Excellent verbal and written communication skills. Ability to explain complex concepts and results to both technical and non-technical stakeholders.
• Strong problem-solving abilities with a focus on innovative solutions for model development and training.