I build automated, resilient cloud systems ready for real-world scale.

I'm Desiree Weston, a DevOps & Cloud Engineer focused on AWS, serverless architecture, and CI/CD automation. My philosophy is simple: excellent infrastructure should stay out of the spotlight. It should deploy itself, heal itself, and quietly support whatever the product team dreams up next.

My background in enterprise software implementation taught me how to design systems that actually solve business problems, not just pass certification exams. Now I bring that same discipline to cloud engineering:

• Infrastructure as Code for repeatable, production-grade environments

• CI/CD pipelines that ship changes safely and quickly

• Serverless and containerized workloads built for efficiency

• Monitoring and logging that catch issues before users do

I'm actively leveling up through Kubernetes training and hands-on projects that mirror how modern engineering teams work. Every project on this site includes authentic architecture, real automation, and real lessons learned—not just screenshots.

What motivates me? The moment when everything flows. When the pipeline runs clean on the first try, when the rollback is instant, when an environment spins up from a single template instead of a weekend of manual setup, that's the part of engineering I love, the quiet reliability built from intentional design.

As an AWS Certified Cloud Practitioner, Developer Associate, and AI Practitioner, I focus on practical cloud engineering that teams can trust: secure by default, cost-aware, and built with automation from day one.

If you're building with AWS and want someone who treats infrastructure as a craft, not an afterthought. I'm glad you're here.

Building Expertise Through Continuous Learning

Current Certifications

AWS Certified Developer – Associate

Amazon Web Services | Achieved: [11/6/2025]

Key Competencies:

• Deep understanding of AWS core services with a focus on building, deploying, and debugging cloud-native applications

• Proficient in developing serverless solutions using AWS Lambda, API Gateway, DynamoDB, SQS, SNS, and EventBridge

• Skilled in writing, deploying, and maintaining applications using CI/CD pipelines with CodeBuild, CodeDeploy, and CodePipeline

• Strong grasp of AWS security best practices, including IAM, KMS, encryption, and application-level access control

• Expertise in interacting with AWS services through SDKs, CLI, and automation tools

• Hands-on experience with monitoring, logging, and performance optimization using CloudWatch and X-Ray

• Familiar with container-based development using ECS, ECR, and basic Docker workflows

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AWS Certified Cloud Practitioner

Amazon Web Services | Achieved: [3/10/2025]

Key Competencies:

• AWS Cloud value proposition and economics

• Security and compliance within the AWS Cloud

• Core AWS services across compute, network, databases, and storage

• Billing, account management, and pricing models

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| View on Credly

AWS Certified AI Practitioner

Amazon Web Services | Achieved: [7/29/2025].

Key Competencies:

• Fundamentals of AI, ML, and generative AI technologies

• Practical applications of AWS AI services (Amazon Bedrock, SageMaker, Rekognition, etc.)

• Responsible AI practices and MLOps principles

• Designing intelligent solutions that combine cloud infrastructure with AI capabilities

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Certification Roadmap

2025 Goals:

• Certified Kubernetes Administrator (CKA) - Q1 2026

• AWS Machine Learning Associate - Q1 2026

Last Updated: [Novv25]

My Projects

I’ve developed AWS and AI projects that highlight my skills in cloud development and DevOps. From serverless web apps to AI-powered tools, my work demonstrates expertise with Lambda, API Gateway, DynamoDB, Bedrock, and CloudFormation, as well as modern CI/CD and Infrastructure as Code practices.

Auto-Scaling Microservice: Homelab + AWS Hybrid

Designed and deployed a microservice that auto-scales from my home lab to AWS under real-world traffic spikes.

• Role: Cloud / DevOps Engineer

• Stack: Kubernetes · AWS EC2 · ALB · Terraform/CloudFormation · GitHub Actions · Prometheus/Grafana

• Type: Personal Lab → Production-Ready Pattern

About the project:

• Built a hybrid setup that can burst from homelab to AWS during peak load.

• Implemented auto-scaling, health checks, and blue-green deployments.

• Cut “manual babysitting” of services to near zero with observability + alerts.

Go-Dev — High-Performance Go Microservice

Developed a lightweight Go microservice built with clean architecture, automated testing, and a full CI/CD pipeline designed for cloud-ready deployments.

Role: Developer / DevOps Engineer

Stack: Go · Docker · Gin/Fiber/Chi · PostgreSQL/DynamoDB · GitHub Actions · IaC · Prometheus/CloudWatch

Type: Personal Lab → Production-Ready Developer Service

About the project:

• Built a high-performance Go API using clean architecture patterns for scalability and long-term maintainability.

• Implemented automated testing, linting, and container builds wired into GitHub Actions for push-button CI/CD.

• Designed the service to deploy cleanly to cloud environments using Docker + IaC, with health checks and structured logging baked in.

• Added observability with metrics, logs, and performance dashboards — making debugging and scaling predictable.

AI Content Generator

An AI-powered text generation tool using Amazon Bedrock, Lambda, and API Gateway, with a Streamlit frontend for user interaction. Showcases generative AI integration with AWS services for on-demand content creation.

DevOps Home Lab: Local Infrastructure for Real-World Cloud Engineering

Local Infrastructure for Real-World Cloud Engineering
A full multi-node DevOps environment built on Apple Silicon using VMware, Vagrant, Kubernetes, and CI/CD designed to simulate production infrastructure without cloud cost.

Role: Cloud / DevOps Engineer
Stack: VMware Fusion · Vagrant · Kubernetes · Docker · GitHub Actions · Terraform · Prometheus/Grafana
Type: Personal Lab → Production Simulation

Highlights:

• Built reproducible virtualized infrastructure for testing real-world DevOps workflows.

• Deployed Kubernetes clusters, CI/CD, monitoring, and load testing.

• Safe, cost-free sandbox for breaking, fixing, and tuning production-style systems.

Blog Posts