Cutting Cloud Spend in Half Without Touching SLAs
Multi-cloud environment · AWS + GCP · Fintech workloads
Infrastructure costs were growing faster than revenue. Ran a systematic FinOps audit: Spot Instance migration for stateless workloads, Savings Plans for baseline compute, rightsizing 200+ EC2 instances, and architectural consolidation of redundant services. Zero SLA degradation, zero prod incidents during migration.
30–50% cost reduction
0 SLA breaches
3 cloud providers
From Weekly Releases to Daily Deploys
Multi-team product org · GitLab CI → GitHub Actions + ArgoCD
Release cycles were slow and error-prone — manual steps, inconsistent environments, and deployment anxiety. Rebuilt pipelines end-to-end: standardized environments with Terraform, introduced progressive delivery (blue-green + canary), rolled out GitOps with ArgoCD. Change failure rate dropped below 2%, MTTR under 10 minutes.
45% faster releases
<2% failure rate
70% less manual work
Building Observability That Actually Works at Scale
High-throughput platform · VictoriaMetrics + Prometheus + Loki + Grafana
The existing monitoring was fragmented — infra metrics in one place, app metrics in another, business metrics nowhere. Built a unified observability stack processing 4M+ metrics/min with 4+ trillion datapoints stored, automated incident routing to PagerDuty, and custom dashboards for engineering and business stakeholders alike.
4M+ metrics/min
4T+ datapoints
1 unified stack
Shifting Security Left Across the Entire SDLC
Multi-team engineering org · Fintech compliance requirements
Security was reactive — vulnerabilities found in prod, secrets occasionally committed to Git, IaC configs drifting from policy. Embedded tfsec, Checkov, Snyk, and TFLint as mandatory CI/CD gates. Standardized secrets handling with Vault + AWS Secrets Manager. Centralized SSO via SAML 2.0/OAuth2. Supported full SOC 2 readiness audit end-to-end.
100% IaC policy gates
SOC 2 compliant
0 secrets in Git
ML Platform That Data Scientists Actually Use
Big Data domain · Airflow MWAA + MLflow + Argo Workflows + Kafka + Spark
Data science team was bottlenecked on infra — model training jobs competing for resources, no reproducible environments, retraining pipelines requiring manual intervention. Built end-to-end ML infrastructure: managed Airflow for orchestration, MLflow for experiment tracking and model registry, Argo Workflows for scalable training, Kafka + Spark for feature pipelines.
0 manual retrain steps
Full experiment tracking
Auto scaling training
DR Strategy That Survives a Region Outage
Multi-region AWS architecture · Fintech & eCommerce
The DR plan existed on paper but had never been tested. Designed and implemented a real multi-AZ, multi-region architecture with automated failover, database replication, and runbook-driven recovery. Conducted regular DR drills. Achieved RTO under 10 minutes and near-zero RPO — tested, not estimated.
<10 min RTO
~0 RPO
Tested not estimated