NLP (Generative AI_LLM)

About the job

You are someone with:

• LLM Architecture: Good understanding of the architecture underlying large language models, such as Transformer-based models, including GPT and their variants.
• Language Model Training and Fine-Tuning: Experience in training large-scale language models from scratch, as well as fine-tuning pre-trained models on domain data.
• Data Preprocessing for NLP: Skills in preprocessing textual data, including tokenization, stemming, lemmatization, and handling of different text encoding.
• Transfer Learning and Adaptation: Proficiency in applying transfer learning techniques to adapt existing LLMs to new languages, domains, or specific business needs.
• Handling Ambiguity and Context in Text: Ability to design models that effectively handle ambiguities, nuances, and context in NLP.
• Data Annotation and Evaluation: Skills in designing and implementing data annotation strategies for training LLMs and evaluating their performance using appropriate metrics.
• Scalability and Deployment: Experience in scaling LLMs for production environments, ensuring efficiency and robustness in deployment.

What You Will Do

• Model Training, Optimization, and Evaluation: This encompasses the complete cycle of training, fine-tuning, and validating language models. You will be designing and adapting LLMs for use in virtual assistants, Information retreival and extraction etc.
• Experimentation with Emerging Technologies and Methods: Actively exploring new technologies and methodologies in language model development, including experimental frameworks and software tools.

Basic Qualifications

4+ years’ experience writing code using Python
4+ years’ experience in natural language processing tools, including some of the following:
working with large language models
text classification
natural language generation
Bachelor's degree with 2+ years of experience as a machine learning engineer or researcher.

Preferred Qualifications

• A thorough understanding of machine learning, particularly deep learning techniques, including knowledge of neural network architectures, training methods, and optimization algorithms.
• Knowledge and practical experience in NLP techniques and tools, including working with language models, text classification and natural language generation.
• Experience with frameworks: PyTorch, or Keras.
• Skills in data preprocessing, cleaning, and analysis, with the ability to work with large datasets and extract meaningful insights.
• The capacity to think critically and tackle complex, innovative problems in the field.
• Willingness to continuously learn and adapt, crucial in the ever-evolving field of AI.