Pfizer logoCurrently an Extern @Pfizer·🟢 Open to work
Valandy Pierre portrait

Aspiring Manufacturing Engineer

I'm a UMass Amherst senior specializing in Biomedical Engineering, ready to tackle real-world challenges with innovative solutions.

Work samples

Explore my work in biomedical engineering, including AI-driven document insights and data extraction for real-world applications.

  • Pfizer Advanced: AI-Powered Document Insights & Data Extraction Externship
    Pfizer Advanced: AI-Powered Document Insights & Data Extraction Externship
    Pfizer logo

    Pfizer · ✅ Verified by Extern · ⏱️ In progress

    Pfizer Advanced: AI-Powered Document Insights & Data Extraction Externship

    Prototype AI-powered document intelligence with Pfizer—using OCR, LLMs, and RAG to automate real enterprise PDF workflows and build a standout portfolio project.

    AI & MLPythonDocument IntelligencePresentation Skills

About me

I'm a UMass Amherst senior specializing in Biomedical Engineering, ready to tackle real-world challenges with innovative solutions.

I am a senior at the University of Massachusetts Amherst pursuing a Bachelor's Degree in Biomedical Engineering. My journey is just beginning, but I'm eager to apply my skills and knowledge in innovative ways.

Externships

Pfizer Advanced: AI-Powered Document Insights & Data Extraction Externship

Pfizer

Education

University of Massachusetts Amherst

B.S. Biomedical Engineering · Class of 2027

Skills

Biomedical EngineeringArtificial IntelligenceData ExtractionDocument IntelligenceOCRLLMsRAGPythonSERS

✅ Verified by Extern · ⏱️ In progress

Pfizer Advanced: AI-Powered Document Insights & Data Extraction Externship

Prototype AI-powered document intelligence with Pfizer—using OCR, LLMs, and RAG to automate real enterprise PDF workflows and build a standout portfolio project.

AI & MLPythonDocument IntelligencePresentation Skills

Overview

The work examined how AI systems read and represent pharmaceutical documents, combining NLP, LLM concepts, and OCR techniques. The project summarized how text is converted into numerical vectors, how models are trained on corpora to predict or fill tokens, and how vectors are decoded into human-like output. The deliverable documented these mechanisms as they apply to scanned and born-digital

Pfizer Advanced: AI-Powered Document Insights & Data Extraction Externship

What I've accomplished

I produced a technical explanation showing how LLMs convert text into numerical vectors, are trained to predict or fill tokens, learn language patterns, and decode vectors back into human-like text.

Project breakdown

I described how LLMs converted input text into numerical vectors, were trained on corpora to predict next tokens or fill gaps, learned language patterns and grammar, and decoded vectors to generate human-like output.

View all works