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.