Architecture Assessment
Find bottlenecks, risks, coupling, failure modes, and delivery blockers before they reach production.
I architect multi-region, high-load systems — and accelerate delivery with AI agents, with a human owning every decision.

Systems where latency, consistency, and failover are non-negotiable — and governance is designed in, not bolted on.
Find bottlenecks, risks, coupling, failure modes, and delivery blockers before they reach production.
Design failover, traffic steering, consistency strategies, observability, and safe operations across regions.
Build agentic workflows for planning, coding, reviews, testing, documentation, and automation.
Improve Kubernetes, AWS, Terraform, CI/CD, security posture, monitoring, and cost efficiency.
Hands-on architecture leadership for teams that need senior technical direction.
Help engineers unblock complex technical decisions and raise system design quality.
Contributed to the evolution from a C++ monolith into a large microservices platform — geo-routing, failover flows, cross-region context transfer, consistency strategies, observability, and production ownership.
Designed and operated backend services, cloud infrastructure, monitoring, alerting, and abuse / DDoS response for a fast-moving startup environment.
Delivered backend features for Caesars Casino — event-driven high-load product mechanics and production stability work on the Java / Spring Boot ecosystem.
Contributed Cooperative Rebalance support (PR #1081) to node-rdkafka, reinforcing deep Kafka and distributed-systems expertise.
Read the codebase, traffic, and incidents to find the real blockers.
Model load, data flows, consistency, and the failure modes that bite.
Resilient architecture with trade-offs written down, not implied.
Incremental rollout behind flags, with a tested rollback path.
SLO dashboards and Datadog / Grafana monitors that trigger real action.
Encode delivery into agentic, repeatable workflows.
I use AI agents to take on the unglamorous 80% of delivery — planning, implementation, review, tests, and docs — so senior time goes to the decisions that actually need judgment. The pipeline below is how that runs.
The layers I design, own, and operate — follow the signal from the edge down to security.
I started in science, working with sensors, bare-metal microcontrollers, and data-acquisition systems. That early work taught me a standard I still apply today: take something noisy, complex, and unpredictable, and make it reliable enough for real decisions.
Since then I've built across the stack: mobile and desktop apps, frontend systems, high-load backend platforms, games, big-data pipelines, and the CI/CD infrastructure behind them. I've worked in early-stage startups and established product companies, where architecture has to survive real users, real traffic, and real business pressure.
Over time, my focus moved toward the harder end of software engineering: geo-distributed platforms, live replication, cross-region user-data transfer, Kafka-based event systems, and cloud-native products taken from zero to production.
What keeps me in the field is the difficulty. I like problems where the answer is not obvious, the trade-offs matter, and the system has to keep working after the first clean diagram is gone. I stay close to modern tooling, including AI agents, but I believe the important decisions still need human ownership.
My priority is to get the architecture right early, so the business can grow on it without needing a rewrite when scale, reliability, or requirements change.
In 30 minutes we'll pressure-test your architecture or delivery bottleneck and leave you with 2–3 specific, prioritized moves — whether or not we work together.