Deep Knowledge of LLM Architecture: Comprehensive understanding of the architecture underlying large language models, such as Transformer-based models, including GPT (Generative Pre-trained Transformer), 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 for specific applications or domains.
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 craft models that optimally handle ambiguities, nuances, and context in NLP.
Innovative Application of LLMs: Experience in creatively applying LLM technology in diverse areas such as chatbots, content creation, semantic search, and more.
Data Annotation and Evaluation: Skills in crafting 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, automated chatbots, content recommendation systems, etc.
Algorithm Development for Enhanced Language Understanding: Focusing on the development or refinement of algorithms to improve the efficiency and accuracy of language models and understanding and generation tasks.
Applying LLMs to Cybersecurity: Tailoring language models for cybersecurity purposes, such as analyzing threat intelligence, detecting cyber threats, and automating responses to security incidents.
Experimentation with Emerging Technologies and Methods: Actively exploring new technologies and methodologies in language model development, including experimental frameworks and software tools.
Mentoring and Teamwork: Providing mentorship to team members and working collaboratively with disparate teams to ensure cohesive development and implementation of language model projects.
Basic Qualifications
Python
4+ years’ experience in natural language processing tools, including some of the following:
large language models, Gen AI & ML (Exp in leading small teams)
text classification
sentiment analysis
natural language generation
BA / BS degree with 8+ years' experience (or) MS degree with 6+ years of experience (or) PHD + 3 years 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, sentiment analysis and natural language generation.
Experience with frameworks including TensorFlow, PyTorch, or Keras.
Skills in data preprocessing, cleaning, and analysis, with the ability to work with large datasets and extract meaningful insights.
A background in conducting research, crafting experiments, publishing papers, and keeping current with the latest advancements in LLMs.
The capacity to think critically and take on sophisticated, innovative problems in the field.
A willingness to continuously learn and adapt, crucial in the ever-evolving field of AI.