Architected enterprise AKS platform using Terraform & GitOps (ArgoCD). Eliminated configuration drift across 15+ engineering teams; reduced cluster provisioning from hours to under 15 minutes with modular Terraform and self-service infrastructure.
Reduced deployment time by 45% via multi-stage CI/CD pipelines (GitHub Actions, Azure Pipelines) with progressive delivery — canary, blue-green, and A/B deployments via Argo Rollouts. Achieved 98%+ release success rate across 40+ microservices with automated rollback on failure.
Cut security vulnerabilities by 40% embedding Checkmarx (SAST), SonarQube, Veracode (DAST), and OPA/Kyverno (policy-as-code) into CI/CD. Implemented zero-trust Istio mTLS, automated Key Vault secret rotation, and Azure Policy for compliance (SOC 2, PCI-DSS).
Sustained 99.99% platform uptime with Dynatrace, Prometheus, Grafana, ELK Stack, and OpenTelemetry. Established SLO/SLI error-budget policies, reduced MTTR by 40%. Built chaos engineering framework (LitmusChaos) for pre-release resilience validation.
Engineered zero-downtime cluster upgrades with HPA/KEDA autoscaling, optimized node pools, and taints/tolerations. Reduced monthly compute costs by 18% (~$2M annual savings) through right-sizing, spot instances, and FinOps dashboards.
Architected enterprise event-driven platform on AKS using Apache Kafka for real-time data streaming. Tuned Kafka clusters (partition optimization, consumer group autoscaling) achieving 35% throughput increase and 20% latency reduction.
Automated PostgreSQL provisioning, schema migrations (Alembic), backup validation, and failover testing via Terraform. Built Python/Shell automation for drift detection, cost analysis, and resource cleanup, saving 6+ engineering hours weekly.
Delivered AI-powered exception analysis platform (RemediAI) — LangGraph AI agents, Azure Service Bus, PostgreSQL, FastAPI, React dashboard. Reduced .NET exception debugging time via intelligent stack trace analysis with LLM-based remediation suggestions.