Artificial Intelligence Lead

3 days ago

Ahmedabad, Gujarat, India beBeeMultimodal Full time US$ 1,20,000 - US$ 1,60,000
Senior Artificial Intelligence Specialist

We are seeking a highly skilled professional to lead the development of cutting-edge AI solutions.

  • To be successful, you will need to have expertise in Multimodal Retrieval-Augmented Generation (RAG) systems and experience with large-scale datasets.

You should be proficient in vector databases (e.g., Pinecone, Milvus, Weaviate, Chroma) and vector search techniques with recommender systems, vector search capabilities. Your experience with LLMs (e.g., GPT, BERT) and their implementation in real-world applications is also essential.

  • Solid understanding of machine learning and deep learning frameworks (e.g., TensorFlow, PyTorch, MLflow etc).
  • Experience working with structured data (e.g., SQL databases) and unstructured data (e.g., text, images, videos).

This role involves designing, developing, and deploying advanced multimodal systems that leverage both text and visual data. You will work on enhancing the performance and accuracy of AI models using various techniques.

Key Responsibilities:
  1. Design, develop, and deploy Multimodal Retrieval-Augmented Generation (RAG) systems that integrate both structured and unstructured data.
  2. Work with large-scale datasets, combining different data types to enhance the performance and accuracy of AI models.
  3. Implement and fine-tune LLMs (e.g., GPT, BERT) to work effectively with multimodal inputs and outputs.
  4. Develop and optimize AI models using vector databases to efficiently manage and retrieve high-dimensional data.
  5. Implement vector search techniques to improve information retrieval from structured and unstructured data sources.
  6. Ensure the scalability and performance of vector-based retrieval systems in production environments.
  7. Implement and fine-tune large language models to handle complex queries involving multimodal data.
  8. Optimize LLMs for specific tasks, such as text generation, question answering, and content summarization, using both structured and unstructured data.
  9. Integrate LLMs with vector databases and RAG systems to enhance AI capabilities.

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