Good DevOps isn't about keeping the lights on — it's about removing the recurring problems that make infrastructure hard to operate in the first place. Each entry below is a real problem I diagnosed, the approach I took, and the outcome it produced.
<div class="card problem"> block.Workloads couldn't keep up with sudden traffic spikes, while over-provisioning to compensate wasted spend during quiet periods.
Designed queue-based autoscaling for Kubernetes workloads using KEDA and live RabbitMQ queue metrics, so capacity tracked demand in real time.
Absorbed spikes reliably without manual intervention and removed the need for permanent over-provisioning.
Static node groups left clusters paying for idle capacity and missing out on Spot savings.
Implemented dynamic node provisioning with Karpenter and shifted suitable workloads onto Spot instances with safe fallbacks.
Cut infrastructure cost meaningfully while keeping application availability intact.
During Aurora failovers, load balancer targets pointed at the wrong database node, causing avoidable disruption.
Built automation to detect failovers and dynamically update network load balancer targets to the new primary.
Improved database resilience and reduced recovery time during failover events.
Logs and metrics were scattered across systems, making incidents slow to diagnose.
Designed a unified logging and monitoring stack with Prometheus, Grafana, Fluentd, and Elasticsearch.
Gave teams a single place to understand system health and shortened time-to-diagnosis.
Manual deployments were error-prone and lacked a reliable audit trail.
Moved deployments to a GitOps model where the desired state lives in version control and is reconciled automatically.
Made releases repeatable, reviewable, and easy to roll back.
Legacy EC2 workloads were hard to scale and operationally heavy to maintain.
Containerised the workloads and migrated them onto managed EKS with appropriate scaling and health checks.
Simplified operations and unlocked elastic scaling for the migrated services.