Infrastructure That Doesn't Fail
High-performance backends and cloud infrastructure. From architecture to automation, I build systems that stay stable under load and remain understandable to operate.
If you’re dealing with slow deployments, fragile environments, recurring outages, or unpredictable costs, this is the category that fixes it. The work is focused on reliability: making failures rarer, less severe, and easier to recover from.
Systems engineering is also about speed: faster builds, faster deploys, faster debugging. A well-designed system helps your team ship changes confidently without turning every release into an incident.
AWS and Azure solutions designed for scalability and cost control. Auto-scaling, load balancing, and infrastructure as code so environments are reproducible. The aim is to remove the “hand-built server” risk and replace it with predictable automation.
Automated pipelines that test, build, and deploy your code. GitHub Actions, Docker, and (when appropriate) Kubernetes—so releases are repeatable and less error-prone. This is how you ship more often without breaking production.
Profiling slow applications, optimizing database queries, and eliminating bottlenecks. Making your software fast in ways that show up in real user experience: lower latency, higher throughput, and fewer timeouts.
Architecture blueprints for complex systems: monolith vs microservices decisions, event-driven design, and distributed patterns when they’re actually necessary. The goal is to keep the system as simple as possible while still meeting requirements.
Monitoring, alerting, and incident-response readiness so problems are detected quickly and fixed safely. That includes sensible SLOs, logs/metrics that answer questions, and runbooks for common failure modes.
It usually starts with visibility: logs, metrics, and alerts that point to the real failure modes. Then we harden the system: safer deploys, better rollbacks, eliminating single points of failure, and tightening configuration drift. The goal is fewer incidents and faster recovery when something does go wrong.
Yes, when it’s approached carefully. Cost improvements typically come from right-sizing, removing unused resources, improving caching, fixing noisy jobs, and addressing inefficient queries. The key is to measure before and after, and to avoid “optimizations” that introduce fragility.
Absolutely. Small teams benefit most from systems that are simple to operate and safe to change. That means CI/CD that’s reliable, environments that are reproducible, and architecture that fits the team’s capacity. Complexity is only added when the requirements demand it.
A short technical review: how builds and deployments work, where outages have happened, how data is stored, and what the current monitoring looks like. From there we prioritize the highest-risk bottlenecks first—usually deploy safety, database performance, and observability.
Let's discuss your project and see how I can help.