Hands-on experience attacking and defending IoT systems, plus a practiced security playbook for real infrastructure.
Projects come from an IoT cyber defense externship with Hydroficient, covering offensive techniques and implemented defenses, plus the resulting security playbook.
.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.
Hands-on experience attacking and defending IoT systems, plus a practiced security playbook for real infrastructure.
I am an early-career cyber defense enthusiast with hands-on experience from an IoT security externship. I learned attack techniques such as spoofing and replay, and implemented defenses including TLS and mutual TLS while documenting a security playbook for real infrastructure.
Externships
Pfizer Advanced: AI-Powered Document Insights & Data Extraction Externship
In progress
Experience
ROBOTICS DATA SPECIALIST
Micro1 · 07/2026-Present (Part time)
AI EVALUATOR / AI DATA SPECIALIST
Mercor · 08/2025-07/2026
CUSTOMER SERVICE / CUSTOMER SUCCESS PROFESSIONAL
Globe Life · 02/2023-05/2025
CYBERSECURITY / TECHNICAL SUPPORT (6 month contract)
Mercor · 11/2025-04/2026
HEALTHCARE ADMINISTRATION
Casa Trinity · 02/2019-03/2025
EXECUTIVE SUPPORT / ADMINISTRATIVE OPERATIONS
Casa Trinity · 02/2019-03/2025
TEAM LEAD / SUPERVISOR
Cotton Mouth Dispensary · 05/2023-01/2026
Education
Penn foster
Highschool · Class of 2017
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.
This externship prototyped an AI document-intelligence pipeline combining OCR, large language models, and retrieval-augmented generation to process enterprise PDFs. The work documented how core LLM components function and how they integrate with OCR and retrieval services. Deliverables included conceptual diagrams and a technical explanation of LLM mechanics.
I summarized LLM internals, explaining tokenization, embeddings, positional encoding, self-attention, and autoregressive next-token prediction as the basis for model capabilities.
I summarized LLM internals, describing tokenization, embeddings, positional encoding, self-attention, and autoregressive generation, and noted that capabilities arise from next-token prediction.
I worked in Google Colab to run Python notebooks, debugged Pandas parsing errors, processed JSON snippets, performed text cleaning, and applied OpenCV thresholding to enhance a scanned document for OCR. Deliverables included multiple Colab and Drive files.
