I am a Software Engineer and AI Specialist (B.S. Computer Science '25) focused on building intelligent, secure, and scalable cloud systems.

My passion lies at the intersection of AI/Machine Learning, Backend Development, and Application Security. I bring hands-on experience from an AI Engineering Internship at Appgate, where I architected data pipelines for Neo4j knowledge graphs and led R&D in GraphRAG and LLM querying.

• 🌱 I'm currently building TrainTally and deepening my skills in agentic workflows and cloud-native security.
• 🚀 I am actively seeking full-time Software Engineer or AI/ML Engineer opportunities.
• 🔗 Connect with me: LinkedIn

I hold 7 Cloud & AI Certifications, specializing in Machine Learning and Generative AI on AWS.

Here's my professional toolkit, categorized for clarity:

• Programming Languages: Python, Java, JavaScript, Swift, C#, SQL, Kotlin, Cypher
• AI & ML Concepts: Prompt Engineering, LLM Fine-Tuning, Agentic Workflows, RAG (Retrieval- Augmented Generation), Transfer Learning, Large Language Models (LLMs), Foundation Models, NLP, Neural Networks (DNNs, RNNs, CNNs), Knowledge Graphs, Embeddings, Data Engineering, Model Evaluation
• AI & ML Libraries/Tools: Ollama, LlamaIndex, GraphRAG, TensorFlow, XGBoost, Scikit-learn, Pandas, NumPy
• Cloud, Backend & DevOps: Amazon Web Services (AWS), Amazon Bedrock, Amazon SageMaker, Microsoft Azure, Docker, Flask, Streamlit, REST APIs, Git, Linux, SSH, Virtual Machines (VMs)
• Data Visualization: Seaborn, Matplotlib, Plotly Express, Altair
• Security: Burp Suite, Web Vulnerability Analysis, AWS Security
• Databases: MySQL, Neo4j

Here are a few of the projects I'm most proud of, showcasing my hands-on experience.

A smart companion app for Ticket to Ride board games.

• Status: iOS Core Complete ✅ | Cloud Backend In-Progress 🚧
• Tech: Swift 5.9, SwiftUI, SwiftData (Local Persistence), MVVM.
• Overview: Built a dynamic scoring engine supporting multiple game versions (USA, Germany, Old West) with version-specific rules. Features include real-time train car tracking, local game history, and "meeple" scoring logic.
• Next Steps: currently architecting a serverless AWS backend (Lambda, DynamoDB, Cognito) for cloud-synced leaderboards and family groups.

An AI-powered Q&A system for religious texts.

• Tech: Python, Neo4j, GraphRAG (LlamaIndex), Ollama, Flask, React Native.
• Impact: Engineered a pipeline to process thousands of texts into a knowledge graph, enabling natural language querying via LLMs.

Machine Learning for cybersecurity.

• Tech: Python, TensorFlow, XGBoost, Scikit-learn, Pandas, NumPy.
• Impact: Built a custom intrusion detection model achieving 90% accuracy on the 2.8M record CICIDS2017 dataset.

Deep Learning for medical imaging.

• Tech: Python, TensorFlow, Keras, Transfer Learning (InceptionV3), Scikit-learn, Pandas, NumPy.
• Impact: Achieved 98% accuracy in classifying MRI images, outperforming baseline custom CNNs.