Senior Gen AI/LLM Architect

2 weeks ago

Raritan, United States Pozent Full time

JD:

  • Must have 15+ 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 (Client) 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.
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