About Me
I’m a software engineer with over four years of experience building secure, large-scale backend systems. My core stack includes AWS, Node.js, and Python. As an AWS Certified Solutions Architect, I spend a lot of my time focused on the critical mechanics of a system: keeping uptime high, tracking down latency bottlenecks, and using Terraform so deployments are actually painless.
Recently, I’ve been heavily focused on machine learning engineering. I'm taking my background in robust infrastructure and applying it to fine-tuning LLMs, optimizing GPU workloads, and building AI-driven applications. Ultimately, I enjoy the challenge of taking complex architectures and making them run smoothly and reliably in production.
Education
Master of Science in Natural Language Processing
University of California, Santa CruzGPA: 3.85/4.0
Relevant Coursework: Deep Learning, Machine Learning, Conversational Agents.
Bachelor of Science in Mathematics
University of South DakotaGPA: 3.4/4.0
Relevant Coursework: Linear Algebra, Abstract Algebra, Real Analysis, Numerical Analysis, Advanced Calculus.
Core Competencies
Backend
Python, FastAPI, Node.js, Express, WebRTC, WebSockets, REST APIs
Infrastructure
AWS (EC2, S3, Lambda, auto-scaling, load balancing), Terraform, Docker, CI/CD, PostgreSQL, Redis, MongoDB, Linux
Machine Learning
Agentic AI, LangChain, AutoGen, RAG, PyTorch, Transformers, DeepSpeed, Slurm, Scikit-learn, Fine-tuning LLMs, Quantization
Frontend
React, Next.js, Flutter, Three.js