Building production-ready AI infrastructure on AWS
Cloud & MLOps Engineer building production-ready AI infrastructure.
From model training to deployment at scale.
| 🏢 | Senior AWS Cloud & MLOps Engineer at TPS |
| 🎓 | Electronic Engineering - University of Cauca |
| 📅 | 5+ years of experience (3 Software Engineering + 2 Cloud Engineering) |
| ☁️ | 6x AWS Certified | ML Specialty & Solutions Architect & DevOps |
| 🎯 | Focus: MLOps, LLMOps, Kubernetes (EKS), GPU Infrastructure |
| 🌍 | Creating bilingual content (EN/ES) about Cloud, MLOps & GenAI |
-326CE5?style=flat-square&logo=kubernetes&logoColor=white){kind=icon}
Building end-to-end ML pipelines • Deploying LLMs at scale • Optimizing AI infrastructure costs
| Project | Description | Tech |
|---|---|---|
| E2E-EKS-GitOps | Full MLOps platform on EKS with GitOps |
|
| ml-recommendation-systems | Neural Collaborative Filtering + Amazon Personalize + A/B Testing |
|
| GitHubActions-EKS | CI/CD to EKS with auto-scaling |
|
| ml-lambda-deployment | Serverless ML inference with property-based testing |
|
| kiwibot-by-xero-chatgpt-test | AI-powered support system with multi-LLM orchestration |
|
Open to collaborations on MLOps, GenAI, and Cloud-native AI projects!
💬 Let's talk about: ML Pipelines • LLM Deployment • GPU Optimization • FinOps for AI