Gen AI/LLM Data Scientist

Role : Senior Gen AI/LLM Data scientist

Location : Raritan, New Jersey

2 levels of interview, No hybrid/No work from home. All days onsite

Must have 10+ years of experience working in Data science, Machine learning and especially NLP technologies.

• Exposure to various LLM technologies and solid understanding of Transformer Encoder Networks.
• Able to apply deep learning and generative modeling techniques to develop LLM solutions in the field of Artificial Intelligence.
• Utilize your extensive knowledge and expertise in machine learning (ML) with a focus on generative models, including but not limited to generative adversarial networks (GANs), variational autoencoders (VAEs), and transformer-based architectures.
• Solid understanding of Model development, model serving, training/re-training techniques in a data sparse environment.
• Very good understanding of Prompt engineering techniques in developing Instruction based LLMs.
• Must be able to design, and implement state-of-the-art generative models for natural language processing (NLP) tasks such as text generation, text completion, language translation, and document summarization.
• Work with SAs and collaborate with cross-functional teams to identify business requirements and deliver solutions that meet the customer needs.
• Passionate to learn and stay updated with the latest advancements in generative AI and LLM.
• Nice to have -contributions to the research community through publications, presentations, and participation in relevant conferences or workshops.
• Evaluate and preprocess large-scale datasets, ensuring data quality and integrity, and develop data pipelines for training and evaluation of generative models.
• Ability to articulate to business stakeholders on the hallucination effects and various model behavioral analysis techniques followed.
• Exposure to developing Guardrails for LLMs both with open source and cloud native models.
• Collaborate with software engineers to deploy and optimize generative models in production environments, considering factors such as scalability, efficiency, and real-time performance.
• Nice to have- provide guidance to junior data scientists, sharing expertise and knowledge in generative AI and LLM, and contribute to the overall growth and success of the data science team.