Architect and implement state-of-the-art generative models for complex NLP tasks.
Responsibilities
- Design and implement generative models for text generation, completion, translation, and summarization.
- Develop data pipelines to evaluate and preprocess large-scale datasets for model training.
- Collaborate with software engineers to deploy and optimize models for scalability and real-time performance.
- Build and implement guardrails for both open-source and cloud-native LLMs.
- Articulate model behavioral analysis and hallucination effects to business stakeholders.
Required Skills
- 15+ years of experience in Data Science, Machine Learning, and NLP technologies.
- Deep expertise in Transformer Encoder Networks and LLM architectures.
- Extensive knowledge of generative modeling including GANs, VAEs, and transformer-based architectures.
- Proven experience in model development, serving, and training/re-training in data-sparse environments.
- Advanced proficiency in prompt engineering for instruction-based LLMs.
- Ability to apply deep learning and generative modeling techniques to solve AI problems.
- Experience with large-scale dataset preprocessing and data integrity management.
- Strong understanding of model behavioral analysis and hallucination mitigation.
Preferred Skills
- Contributions to the research community via publications or conference presentations.
- Experience providing technical guidance and mentorship to junior data scientists.