Pfizer Externship via Extern
I prototype AI systems that extract and summarize data from enterprise PDFs using OCR, LLMs, and RAG to make document workflows faster and more reliable.
Portfolio work centers on prototypes that automate extraction, indexing, and question-answering over enterprise PDFs, combining OCR, LLMs, and RAG methods.
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Pfizer · ✅ Verified by Extern · ⏱️ In progress
Prototype AI-powered document intelligence with Pfizer—using OCR, LLMs, and RAG to automate real enterprise PDF workflows and build a standout portfolio project.
Built a Python S3-to-Snowflake pipeline with dbt, processing 10M+ orders and 23M+ order items Enriched 300,000+ reviews with OpenAI GPT-4o-mini into structured sentiment and topic fields Implemented a RAG chat interface over review embeddings and a guarded text-to-SQL assistant served through Streamlit Orchestrated the load-transform-enrich-analyze pipeline as a daily Airflow DAG running in Docker
I prototype AI systems that extract and summarize data from enterprise PDFs using OCR, LLMs, and RAG to make document workflows faster and more reliable.
I hold a master’s degree and I am early in my career while exploring a shift in direction. I build practical prototypes that apply OCR, large language models, and retrieval-augmented generation to real PDF workflows. I completed a Pfizer Advanced externship focused on document intelligence to strengthen my portfolio and job readiness.
Externships
Pfizer Advanced: AI-Powered Document Insights & Data Extraction Externship
In progress
Experience
AI Engineering Fellow
Code Path · Sep 2026 – Present
Software Engineer
ITC Infotech · Aug 2022 – Jun 2024
Associate Software Engineer
ITC Infotech · Jan 2022 – Jul 2022
Education
Binghamton University, SUNY
Master of Science in Computer Science | (GPA: 3.67 / 4.00) · Class of 2026
Skills
✅ Verified by Extern · ⏱️ In progress
Prototype AI-powered document intelligence with Pfizer—using OCR, LLMs, and RAG to automate real enterprise PDF workflows and build a standout portfolio project.
The externship produced a prototype workflow that connected OCR, retrieval-augmented generation, and language models to process enterprise PDFs. Deliverables included a survey of LLM architectures and technical notes describing component interactions. The work documented how models handle structured and unstructured pharmaceutical documents.
I documented LLM behavior and training: defining an LLM as a predictive-text model trained on billions of pages, outlining its masked/next-word training dynamics, noting human-guided fine-tuning, and clarifying it does not learn from live chats after training.
I described an LLM as a predictive-text model trained on billions of pages to predict the next word, explained its practice-of-hidden-words training, and noted human-guided fine-tuning and that the model does not learn from live chats after training.
Built a Python S3-to-Snowflake pipeline with dbt, processing 10M+ orders and 23M+ order items Enriched 300,000+ reviews with OpenAI GPT-4o-mini into structured sentiment and topic fields Implemented a RAG chat interface over review embeddings and a guarded text-to-SQL assistant served through Streamlit Orchestrated the load-transform-enrich-analyze pipeline as a daily Airflow DAG running in Docker
Cleaned and analyzed 10,000+ collision records using Python and Pandas, resolving missing values and data-quality issues prior to statistical analysis. Developed 15+ visualizations using Matplotlib and Seaborn to identify and communicate crash trends by time, contributing factor, vehicle type, and borough.
