Data Model Specialist

Reimagine the power of language to drive business growth.

As an LLM Engineer, you will leverage cutting-edge NLP and machine learning technologies to build, fine-tune, and deploy state-of-the-art language models. You will collaborate with cross-functional teams to create innovative solutions that unlock the full potential of large language models for a variety of applications.

• Bachelor's or master's degree in computer science, natural language processing, or a related field.
• 4+ years of experience working with large language models (LLMs) or NLP-related technologies.
• Solid understanding of prompt engineering principles and experience in optimizing prompts for tasks such as text generation, classification, and summarization.
• Strong proficiency in Python and experience with machine learning libraries such as TensorFlow, PyTorch, Transformers, or similar frameworks.
• Experience with large-scale model training and fine-tuning for NLP tasks using tools like Hugging Face, OpenAI, or Google AI.
• Solid understanding of natural language processing (NLP) techniques and algorithms, including tokenization, embeddings, transformers, and attention mechanisms.
• Familiarity with cloud computing platforms (e.g., AWS, Google Cloud, Azure) for model training and deployment.
• Experience in data preprocessing and handling large, unstructured text datasets.
• Strong problem-solving skills and the ability to work with complex, unstructured data.
• Good communication skills, with the ability to present technical concepts to both technical and non-technical stakeholders.
• Experience with transformer-based architectures such as GPT-3, BERT, T5, or similar models.
• Knowledge of distributed computing and working with large-scale training infrastructure (e.g., using GPUs/TPUs).
• Familiarity with MLOps practices for model versioning, deployment, and monitoring.
• Experience with model explainability techniques, model debugging, and interpretability.
• Exposure to fine-tuning large models for specific industries or use cases (e.g., healthcare, finance).
• Contributions to open-source projects or research publications in the NLP/ML domain.

• Design, develop, and fine-tune large language models (LLMs) for various NLP tasks, such as text classification, sentiment analysis, language generation, question answering, summarization, and more.
• Collaborate with data scientists, researchers, and software engineers to integrate LLMs into production environments and ensure they meet performance, scalability, and reliability requirements.
• Conduct prompt engineering experiments to improve model outputs, including exploring different prompt formats, phrasing, and model configurations.
• Experiment with and implement the latest advancements in natural language processing (NLP) and machine learning to improve model performance and efficiency.
• Preprocess and clean large-scale text datasets, ensuring they are suitable for training and fine-tuning LLMs.
• Optimize LLMs for speed, accuracy, and memory efficiency, ensuring that they can run effectively in a production environment.
• Evaluate and benchmark different LLM architectures, such as transformer-based models (e.g., GPT, BERT, T5), and apply appropriate strategies for different business needs.
• Conduct testing, validation, and troubleshooting of models to ensure robustness and reliability.
• Stay up-to-date with developments in the field of NLP, deep learning, and large language models to ensure that our solutions remain competitive and innovative.
• Collaborate with product managers and other stakeholders to define project goals, requirements, and timelines.