Duties and Responsibilities include but are not limited to:

• Develop and evaluate machine learning and deep learning models for pharma-related document analysis.
• Build pipelines for OCR/OMR and extract structured information from unstructured text (clinical forms, prescriptions, regulatory filings, etc.).
• Integrate and experiment with LLMs like Qwen, Nuextract, and other open-source foundation models using LangChain.
• Write scalable, testable code in Python and occasionally in Java, especially for back-end or integration tasks.
• Assist in creating prompt templates, retrieval-augmented generation (RAG), and LLM-enhanced search capabilities.
• Support data cleaning, annotation, and labeling tasks in medical/NLP datasets.
• Collaborate with data scientists and domain experts to improve model accuracy and performance.

The duties set forth above are essential job functions for the role. Reasonable accommodations may be made to enable individuals with disabilities to perform essential job functions.

Skills and Qualifications

• Strong coding proficiency in Python and Java.
• Solid understanding of machine learning, deep learning, and NLP fundamentals.
• Exposure to or interest in LLMs, LangChain, Qwen, Nuextract, or other instruction-following/foundation models.
• Hands-on experience or coursework in OCR/OMR, computer vision, and document data extraction.
• Familiarity with libraries such as Transformers (Hugging Face), OpenCV, Tesseract, SpaCy, or PyTorch/TensorFlow.
• Ability to work independently and collaborate in cross-functional teams.
• Background in Biomedical AI, Healthcare Informatics, or Pharmaceutical NLP projects.
• Experience working with large PDF/TIFF document corpora and associated annotation tools.
• Knowledge of information retrieval, prompt engineering, and LLM deployment techniques.