AWS DevOps Engineer and Cloud Architect based in Mexico, working US Central hours.
I'm an AWS DevOps engineer with more than 17 years in IT. I build and repair AWS infrastructure and delivery pipelines using Terraform, AWS CDK, CI/CD and containers. I document the plan before making any change, submit pull requests for your team's review, and remove the access you gave me when I hand over the work.
Each service links to a public repository with a sample deliverable or working code you can inspect.
- Terraform on AWS audit and fix: a ranked diagnosis of risky infrastructure code, fixes as pull requests and a
state migration that destroys nothing. Evidence:
terraform-aws-rescue-lab - CI/CD pipeline to AWS: keyless deploys through OIDC, with image scanning and automatic rollback. Evidence:
github-actions-aws-oidc-lab - AWS security and IAM review: findings ranked by risk, policy rewrites that narrow access, and a baseline set of
guardrails. Evidence:
aws-iam-security-review-sample - Kubernetes on Amazon EKS: clusters built with Terraform and delivered through Helm and Argo CD. Evidence:
terraform-aws-eks-gitops-lab - Containerize and deploy to ECS Fargate: a repeatable, reversible path from one machine to AWS. Evidence:
aws-ecs-fargate-deploy-lab - AWS landing zone for a new project: separate accounts, central sign-in, an audit trail and tested service
control policies. Evidence:
terraform-aws-landing-zone-lab - DevOps and Well-Architected assessment: a risk-rated backlog and a roadmap your team can execute. Evidence:
aws-well-architected-assessment-sample - AWS cost optimization audit: ranked savings with stated assumptions, plus tagging and ownership for the spend.
Evidence:
aws-cost-optimization-audit-sample - Migration to AWS: a wave plan and a cutover runbook with rollback triggers. Evidence:
aws-migration-runbook-sample - AWS workshop and mentoring: hands-on labs with starter code, solutions and tests. Evidence:
aws-devops-workshop-labs
Every repository uses a fictional client and verifies its evidence offline, without an AWS account.
| Repository | Outcome |
|---|---|
aws-devops-portfolio |
Index of every service, with a check that keeps the whole collection consistent |
terraform-aws-rescue-lab |
Untrusted Terraform turned into tested modules with remote state and a plan gate |
github-actions-aws-oidc-lab |
Long-lived CI keys replaced by OIDC, and deploys that fail when ECS rolls back |
aws-iam-security-review-sample |
Security review report with ranked findings and a control-to-evidence map for SOC 2 |
terraform-aws-eks-gitops-lab |
EKS through Terraform modules, a Helm chart and Argo CD folders per environment |
aws-ecs-fargate-deploy-lab |
API containerized and deployed to Fargate with a circuit breaker and alarm-based rollback |
terraform-aws-landing-zone-lab |
Multi-account foundation with AWS Organizations and guardrails tested against real requests |
aws-well-architected-assessment-sample |
Assessment across the pillars and the DevOps lens, with a ranked backlog and roadmap |
aws-cost-optimization-audit-sample |
Savings split into quick wins and planned work, each recalculated from billing data |
aws-migration-runbook-sample |
Wave plan, AWS DMS tasks and a timed cutover runbook with a way back |
aws-devops-workshop-labs |
Numbered labs on AWS, Terraform, CDK and CI/CD, each tested against its solution |
cdk-python-nag-pipeline-lab |
CDK Pipelines app in Python that stops on any unacknowledged cdk-nag finding |
Clients now want AI in their platforms, not only faster pipelines. What I bring:
- Generative AI platforms on AWS: an internal LLM gateway and GenAI catalog on Amazon Bedrock, with ECS Fargate, API Gateway, Cognito and cross-region inference, designed for a retail chain.
- AI inside delivery pipelines: a CI security scanner that turns scanner output into a written assessment with Amazon Bedrock.
- Agentic tooling for engineering teams: coding assistants such as Claude Code, OpenAI Codex, Amazon Q Developer and Kiro, set up with MCP servers, shared skills and review workflows, plus hands-on workshops so teams adopt them.
- Teaching: an agentic AI unit (fundamentals and MCP) in the systems design course I teach.
AWS, Terraform, AWS CDK, CloudFormation, Amazon EKS, Amazon ECS, Helm, Argo CD, GitHub Actions, GitLab CI, AWS CodePipeline, Docker, Python, Bash, Checkov, Trivy, Amazon Bedrock, Claude Code, OpenAI Codex, MCP.
Adjunct professor of Cloud Architecture and Scalable Systems Design at ITESO, the Jesuit University of Guadalajara.
I work in English and Spanish, and I reply within a business day on weekdays.