Data Scientist

Key Skills:

Roles & Responsibilities:

• Apply deep learning and generative modeling techniques to develop LLM solutions in AI, especially in the field of Natural Language Processing (NLP).
• Work with various LLM technologies and Transformer Encoder Networks to enhance NLP capabilities.
• Design and implement state-of-the-art generative models for tasks such as text generation, text completion, language translation, and document summarization.
• Utilize expertise in machine learning, focusing on generative models like GANs, VAEs, and transformer-based architectures.
• Develop and optimize model development, model serving, and training/re-training techniques in data-sparse environments.
• Apply prompt engineering techniques for developing instruction-based LLMs.
• Collaborate with SAs and cross-functional teams to identify business requirements and deliver solutions that meet customer needs.
• Stay updated with the latest advancements in generative AI and LLM technologies.
• Evaluate and preprocess large-scale datasets, ensuring data quality and integrity, and develop data pipelines for model training and evaluation.
• Articulate model behavior analysis and hallucination effects to business stakeholders.
• Develop guardrails for LLMs, leveraging both open-source and cloud-native models.
• Collaborate with software engineers to deploy and optimize generative models in production environments.
• Mentor junior data scientists and contribute to the growth of the data science team.

Experience Requirement:

• 4-10 years of experience working in Data Science, Machine Learning, and especially NLP technologies.
• Experience with LLM technologies and solid understanding of Transformer Encoder Networks.
• Proven experience with generative models, including GANs, VAEs, and transformer-based architectures.
• Familiarity with model development, training/re-training techniques, and deployment in data-sparse environments.
• Strong understanding of prompt engineering techniques for LLMs.
• Experience with data preprocessing and building data pipelines for generative models.
• Ability to explain model behaviors, hallucination effects, and behavioral analysis techniques to stakeholders.
• Experience with developing guardrails for LLMs using open-source or cloud-native models.
• Exposure to MarkLogic is a plus.

Education: