High-Accuracy BioEntity Recognition

I'm seeking an experienced AI developer with a robust background in Natural Language Processing (NLP) and Machine Learning, especially focusing on bio-medical texts, to build a state-of-the-art model capable of recognizing biomedical entities with high accuracy. The successful implementation of this project will advance our research significantly by enabling precise and efficient analysis of complex biomedical documents.

**Key Requirements:**
- Develop and train a large language model for named entity recognition (NER) in the biomedical domain.
- The model should reliably identify various entities, including diseases, symptoms, and medications.
- Achieve a high level of accuracy in entity recognition to support critical research endeavors.

**Desired Output:**
- The model should output the recognized entities in CSV format, clearly identifying the type of entity (disease, symptom, medication) and the text segment it was identified in.

**Ideal Skills and Experience:**
- Proven experience with NLP and machine learning projects, especially using large language models like BERT or GPT.
- Prior work involving bio-medical named entity recognition or a closely related field.
- Proficiency in Python and related libraries (e.g., TensorFlow, PyTorch, Pandas) for model development and data manipulation.
- Ability to work with large datasets and preprocess them for machine learning tasks.
- Experience in outputting data in various formats, with specific expertise in generating CSV files from complex data sets.

This project not only demands technical excellence but a passion for leveraging AI to unravel the complexities of biomedical texts. If you're skilled in transforming cutting-edge NLP and machine learning technologies into practical solutions and are excited about making a tangible impact in the biomedical field, I look forward to your proposal.