Projects | Cloudyninjas

Projects / Case Studies

Modern platforms, engineered to last

Dummy but realistic outcomes—showing how Cloudyninjas delivers security, reliability, and scalable execution across the stack.

What you get

Clear problem framing and measurable targets.

Security controls aligned to delivery pipelines.

Reliability metrics that guide continuous improvement.

Production-ready operational workflows for cloud and AI.

Secure Landing Zone Modernization

Rebuilt multi-account AWS foundations with policy-as-code and automated guardrails.

Cloud Engineering

Problem

Legacy account sprawl and inconsistent security controls made deployments slow and risky.

Solution

Designed governed landing zones, implemented Terraform modules, and automated compliance checks in CI.

Tech stack

AWSTerraformOPA / RegoKubernetesCloudWatchIAM

Outcome

Reduced provisioning time by 60% and improved policy compliance to 98% within two releases.

DevSecOps Pipeline for Regulated Payments

Hardened CI/CD for sensitive workloads while keeping delivery velocity.

DevSecOps

Problem

Security findings surfaced late, causing rework and missed release windows.

Solution

Introduced SAST/DAST scanning, signed artifacts, and automated remediation gates using policy-as-code.

Tech stack

GitHub ActionsTerraformTrivyOPA / RegoAWS WAFSecrets Vaulting

Outcome

Cut critical findings by 70% and achieved faster approvals with audit-ready evidence.

SRE Observability Upgrade for E-commerce

Built an observability program with SLOs, dashboards, and incident automation.

SRE

Problem

Teams struggled to pinpoint issues; alerts were noisy and incidents took too long to resolve.

Solution

Implemented OpenTelemetry, standardized metrics/logs/traces, and created runbooks tied to SLO burn rates.

Tech stack

OpenTelemetryPrometheusGrafanaLokiKubernetesPagerDuty

Outcome

Improved MTTR by 35% and reduced noisy alerts by 45% after onboarding.

Production MLOps for Clinical Forecasting

Operationalized ML workflows with governance, monitoring, and deployment pipelines.

AI/ML Ops

Problem

Model performance drift went unnoticed and deployments lacked reproducibility controls.

Solution

Created CI/CD for ML, added drift detection, and built model evaluation gates with versioned artifacts.

Tech stack

MLflowKubernetesRayS3W&B / EvalsFeature Store

Outcome

Improved evaluation repeatability and enabled near real-time monitoring with accountable rollbacks.