Eric Borba

I work where AI meets infrastructure — because great models need great plumbing.

   

AI Research (PhD)  →  ML / AI Engineering  →  Cloud Engineering + AIOps
                                              (AWS · Terraform · Observability · FinOps)

Started in academia studying how systems fail — modeling SSD and HDD reliability at HPC scale, published at ARCS 2024, funded by the EU Horizon 2020 IO-SEA project. That work pushed me toward engineering resilient systems at scale.

From there I shipped AI/ML systems to production, then completed the Ironhack Cloud Engineering Bootcamp to own the layer underneath them: AWS infrastructure-as-code, CI/CD, observability, security and FinOps. Today I build AI-powered cloud platforms — where AIOps agents don't just watch infrastructure, they act on it under a human-in-the-loop.

The goal: a Cloud Engineer who speaks fluent AWS and fluent AI — owning the full path from research to production infrastructure.

Cloud & Infrastructure

AI & Machine Learning

Languages, Data & Tooling

A production AWS platform whose core value is an intelligent operations layer: CrewAI agents on Amazon Bedrock that analyze live infrastructure and take supervised actions on it under a 3-tier human-in-the-loop trust model (analysis · autonomous non-prod · approval-gated). Built on full observability (structured logging, custom CloudWatch metrics, Golden-Signal dashboards, tiered alerting), edge security (CloudFront + WAF + GuardDuty + Config/CloudTrail), an Auth0-authenticated operator console, and FinOps cost controls — all Terraform IaC, shipped through GitHub Actions (OIDC, no static keys). Deployed and verified in us-east-1 across 3 AZs.

Highlight: a live operator console with Approve / Reject buttons — the human-in-the-loop, demoed end-to-end. ~$145/mo prod run-rate with deliberate FinOps trade-offs.  🖥️ Live console

Bedrock CrewAI ECS Fargate RDS CloudFront WAF CloudWatch Lambda Auth0 Terraform GitHub Actions

Production-grade AWS 3-tier architecture: internet-facing ALB → 6 Node.js EC2 instances across 2 AZs with Auto Scaling → isolated data tier. Custom VPC with network segmentation, security-group chaining, and CloudWatch monitoring.

Highlight: fully automated scaling and high availability across multiple availability zones.

AWS VPC EC2 Auto Scaling ALB CloudWatch Terraform

The observability foundation the capstone grew from. An order-processing API (FastAPI · ECS Fargate · RDS) with structured JSON logging, 8 custom CloudWatch metrics, Golden-Signal dashboards, tiered SNS alerting, Lambda auto-remediation, FinOps cost monitoring, and AI-powered incident analysis via CrewAI — all Terraform IaC, shipped through GitHub Actions.

Highlight: three injected failure scenarios (error flood, high latency, CPU spike), each diagnosed from the CloudWatch correlation view and remediated automatically by Lambda — closing the alert loop with no human in it.

FastAPI ECS Fargate RDS CloudWatch Lambda SNS Terraform CrewAI

ML-driven reliability analysis of SSD and HDD failure in HPC burst buffers. Uses SMART telemetry from ~1M Alibaba SSDs and Backblaze HDDs to predict Mean Time to Failure with Random Forest and LSTM models.

Highlight: 94% prediction accuracy — published at ARCS 2024, funded by EU Horizon 2020 IO-SEA.

Python MongoDB scikit-learn XGBoost LSTM

🧪 Plus 30+ hands-on labs from the Ironhack Cloud Engineering Bootcamp — Terraform & GitOps workflows, FinOps cost optimization (EC2 scheduling −67%, VPC endpoints −$297/yr), and security & compliance (CIS, WAF, Config, Vault). Browse the ce- repos →

• 🎓 Completed the Ironhack Cloud Engineering Bootcamp — capstone: the AIOps Observability Platform above
• 📜 Preparing the AWS Certified Solutions Architect – Associate and HashiCorp Terraform Associate certifications
• 🏗️ Building production-ready AI services on AWS — containerized, observable, infrastructure-as-code
• 🔍 Going deeper on AIOps, FinOps and MLOps: agentic operations, cost optimization, and model serving on cloud