The job below is no longer available.
You might also like
in Raritan, NJ
Data Scientist
•22 days ago
Estimated Pay | $63 per hour |
---|---|
Hours | Full-time, Part-time |
Location | Raritan, New Jersey 08869 Raritan, New Jersey |
Compare Pay
Estimated Pay We estimate that this job pays $62.53 per hour based on our data.
$34.39
$62.53
$100.92
About this job
Location – Raritan, NJ
4 days Onsite
FTE/C2C
Job Description
· Must have 12+ 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.