Data Innovation Leader

2 days ago

Meerut, Uttar Pradesh, India beBeeAI Full time ₹ 30,00,000 - ₹ 45,00,000

As a leader in cutting-edge AI innovations, we are seeking an experienced Principal Data Scientist to drive the design and development of multilingual Small Language Models (SLMs) with advanced AI capabilities.

About the Role:

This role is pivotal in enhancing agentic applications such as travel destination expertise, Myra bot orchestration, hotel semantic search, and customer support bot assistance.

  • You will lead the design, development, and fine-tuning of SLMs with advanced AI capabilities for agentic applications.
  • You will create custom Natural Language Inference (NLI) and reward-scoring models to improve preference-based SLM training.
  • You will develop and implement active learning pipelines for data and models.
  • You will architect and enhance conversational AI systems to dynamically adapt and comprehend concepts/intents.
  • You will ensure end-to-end quality assurance through robust integration and experience testing.

As a key member of our team, you will collaborate across teams to deliver scalable solutions that impact millions of users, ensuring alignment with business goals. If you are passionate about driving innovation and pushing the boundaries of what is possible with AI, this exciting role may be suitable for you.

What We Offer:

  • A chance to revolutionize the travel industry with cutting-edge AI and Generative AI technologies.
  • A collaborative and dynamic work environment that encourages growth and innovation.
  • The opportunity to work on large-scale projects that impact millions of users worldwide.

Requirements:

  • Deep experience in Natural Language Processing (NLP & NLI), tiny/small language models, base language model retraining.
  • Extensive experience in conversational NLP systems.
  • Experience in SLM adaptor fine tuning, SFT training, SLM pruning.
  • Experience in retriever frameworks in Q&A bots.
  • Experience in contrastive learning, graph learning based embedding methods with heterogenous features.

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