Currently an Extern @Pfizer
Completing a Pfizer externship in AI-powered document insights while growing hands-on skills to make my resume more competitive.
Portfolio items include work from the Pfizer Advanced externship and related projects that show hands-on experience with document insights, data extraction, and applied AI techniques.
Built a Retrieval-Augmented Generation (RAG) AI agent to retrieve relevant data from curated life science libraries and databases. Integrated data cleaning, embedding generation, and retrieval pipelines using Pinecone and AWS. Designed the agent to provide reliable and explainable responses for life
.jpg)
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.
Completing a Pfizer externship in AI-powered document insights while growing hands-on skills to make my resume more competitive.
I am Zakia Djerrah Arib. I hold a masters degree and have some professional experience while I build my career. I am completing the Pfizer Advanced: AI-Powered Document Insights & Data Extraction externship to strengthen my resume and improve my competitiveness for jobs and internships.
Externships
Pfizer Advanced: AI-Powered Document Insights & Data Extraction Externship
Pfizer
Experience
Lab operations lead
Johnson & Johnson · Oct 2022 - Dec 2023
Education
Biology engineer evaluated to Master's in biology
Biology engineering · Class of 2005
Skills
Built a Retrieval-Augmented Generation (RAG) AI agent to retrieve relevant data from curated life science libraries and databases. Integrated data cleaning, embedding generation, and retrieval pipelines using Pinecone and AWS. Designed the agent to provide reliable and explainable responses for life
✅ 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 prototyped an AI document-insights workflow combining OCR, LLMs, and retrieval-augmented generation to process enterprise PDF documents. The work examined model behaviors and pipeline components, and produced explanatory materials about how large language models operate.
I summarized how transformer-based LLMs predict next tokens using self-attention and how fine-tuning plus reinforcement learning from human feedback improve coherence and context-awareness.
The submission summarized how LLMs predicted next tokens using transformer self-attention, learned from billions of examples, and were improved via fine-tuning and reinforcement learning from human feedback to produce coherent, context-aware text.