About Me
I’ve faced the loss of several close relationships during my journey, but those challenges have strengthened my resilience. I’m committed to honoring those I’ve lost by working hard, growing through adversity, and building a future they would be proud of.
I am a seasoned technology professional with a strong foundation from my MS in IT, specializing in enterprise architecture and robust system design. My current focus is on bridging the gap between infrastructure scaling and data science as I pursue my Masters in AI.
My core expertise lies in DevOps Engineering, where I architect and automate highly available, scalable, and secure cloud environments (CI/CD, Infrastructure as Code).
The AI studies allow me to bring a critical MLOps perspective to my engineering, ensuring that machine learning models are not just built, but deployed, monitored, and maintained efficiently in production. I thrive at the intersection of reliable systems and cutting-edge data science.
My Value Proposition
- Scalable Infrastructure: Expertise in Terraform, Kubernetes, and Cloud platforms (AWS/GCP) for high-performance systems.
- AI Deployment: Focusing on MLOps, CI/CD for models, and data pipeline reliability.
- Dual Master's Insight: Bringing advanced knowledge from both IT management and pure AI research.
Technical Toolkit
DevOps & Cloud Engineering
AI, ML & MLOps
Programming & Databases
Key Projects
A selection of demonstrating my capability in both scalable infrastructure and intelligent systems.
Full-Stack MLOps Pipeline on GCP
DevOps / AI
Automated CI/CD pipeline for deploying a neural net model to Google Kubernetes Engine (GKE) using Terraform for infrastructure provisioning and Kubeflow for workflow orchestration.
View CodeTerraform Multi-Cloud Module
DevOps / IaC
Designed and implemented reusable Terraform modules for VPC, compute, and networking across AWS and Azure, reducing infrastructure deployment time by 40%.
View CodeMasters AI Thesis: LLM Fine-Tuning
AI / Research
Research and prototype focusing on fine-tuning small, open-source Large Language Models (LLMs) for domain-specific tasks using LoRA methods and Pytorch.
View Paper/CodeReal-Time Model Monitoring & Drift
MLOps / Data
Built a monitoring system using Prometheus, Grafana, and Evidently AI to track model performance, detect data drift, and trigger automated retraining alerts.
View CodeAcademic Background
Master of Science in Artificial Intelligence
University of the Cumberlands
Relevant Coursework: Deep Learning, MLOps, Statistical Modeling, Big Data.
Master of Science in Information Technology
University of the Cumberlands
Focus: Enterprise Systems, Cloud Computing Fundamentals, Project Management.