I am a Master’s student in Computer Science (AI/ML) at Brigham Young University (GPA: 4.00) with a background in Software Engineering (GPA: 3.87).

I specialize in building production-grade AI systems, designing LLM-powered applications, and deploying scalable ML infrastructure that integrates with enterprise environments.

I focus on delivering measurable impact through:

• ⚙️ AI system architecture & deployment
• 🤖 RAG pipelines & AI agents
• 🧠 Model fine-tuning & LLM inference
• ☁️ Cloud-based ML infrastructure
• 🔁 Automation & enterprise integration

• Architect and maintain Linux-based AI infrastructure and deployment pipelines serving 35,000+ students
• Lead end-to-end AI implementations from design to production across cross-functional teams
• Fine-tune and deploy models that improved system performance by 40%
• Support scalable AI applications integrated into university systems

• Designed and implemented API management solutions integrating enterprise systems and OpenAI connectors
• Built workflow automation systems supporting 200+ IT employees
• Automated 50+ operational processes weekly through event-driven data pipelines
• Delivered production AI-assisted tools integrated into institutional infrastructure

• Contributed to development and deployment of a TTS AI assistant
• Increased organizational productivity by 40% through AI-driven automation
• Integrated AI systems into internal business workflows

• Managed infrastructure incidents during virtualization migration (VMware → Hyper)
• Monitored performance and outages using AWS, Meraki, and AKIPS
• Supported high-availability enterprise systems

Python, C++, C#, Kotlin, JavaScript (React)

PyTorch, TensorFlow, Transformers
RAG architectures, AI agents, NLP, Deep Learning, Computer Vision
LLM fine-tuning, inference optimization, model evaluation

LangChain, LangGraph, LangSmith
OpenAI, Hugging Face, Llama
Pydantic, FastAPI

NumPy, Pandas, Polars, PySpark
Databricks

AWS, Azure
Docker, CI Pipelines
REST APIs, Express.js
Linux environments

n8n, Windmill
Enterprise API integrations
Event-driven workflow automation

• Built an end-to-end AI wardrobe application with two custom-trained ML models
• Designed full ML pipeline: preprocessing, training, evaluation, deployment
• Stack: React/Vue, FastAPI, custom model serving
• Focused on scalable inference and modular architecture

• Developed a multi-agent AI system to automate hypothesis generation, visualization, and code synthesis
• Increased research productivity by 60%
• Built with LangChain, Pydantic, and structured agent orchestration

• Developed a production-ready assistant using React, OpenAI, and RAG architecture
• Implemented vector retrieval, context management, and tool augmentation

• Built a browser extension that converts websites into interactive knowledge sources
• Applied LLM-driven summarization and contextual querying

• Experience shipping AI systems to real users (35,000+ scale environment)
• Strong foundation in software engineering principles and scalable architecture
• Practical experience with LLM orchestration, RAG systems, and AI agents
• Proven impact through automation and measurable productivity gains
• Ability to move from research prototypes to production deployment

• 📫 Email: heychris@byu.edu
• 🌐 Visit my portfolio: heychriss.com

I am always eager to collaborate on new projects and contribute to open-source communities. Feel free to reach out!