SYSTEM ARCHITECT • SENIOR ENGINEER
I ARCHITECT
FAST, SCALABLE
SYSTEMS
AND MAKE THEM
CHEAPER
TO RUN.
I bridge the gap between messy prototypes and production-grade systems — and cut what they cost to run.
BACKGROUND The Engineering Mindset
Before I architected distributed systems, I studied Aerospace Engineering and certified on Boeing 737s.
I bring that same discipline — redundancy, safety factors, and zero tolerance for failure — to your backend architecture. Software shouldn't crash just because it can.
- Safety First Architecture
- Redundant Systems
- Predictable Outcomes
Scaling Without The Bloat
I don't believe in over-engineering. I build boring, predictable systems that just work.
Simplicity first: Complexity is where bugs hide.
Asynchronous by design to prevent cascading failures.
Tech Stack
We accept complexity only where it buys us scale. Otherwise, we stick to standard, boring solutions that are easy to maintain.
Decoupling services to prevent cascading failures. If one part fails, the rest keeps running.
Tracing and metrics are built-in, not an afterthought. We know what broke before the user does.
Measurable Impact
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Audited and refactored cloud infrastructure, reducing monthly AWS spend by ~40%.
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Optimized database queries and caching layers, improving API response times from 300ms to 50ms.
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Architected a system handling 10,000+ concurrent connections with zero downtime during peak load.
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Strangled a monolithic legacy app into microservices with zero downtime — users never noticed.
Engineering as a Revenue Driver
- >> I don't just write code; I solve business problems.
- >> If a feature doesn't add value or reduce friction, we shouldn't build it.
- >> I speak "Executive" and "Developer". I translate business goals into technical roadmaps.
Dev Experience & Velocity
- A slow CI/CD pipeline is a morale killer. I build tooling that allows teams to ship on Friday.
- Good architecture creates good culture. When the system is understandable, onboarding is fast.
- I excel at complex distributed systems and mentor teams to maintain them.
Your Vision
→ Reliable Product
From vague requirements → technical specs → deployment. I handle the chaotic middle so you can focus on the product launch.
AI-Ready Infrastructure HOT
- Vector Database Implementation (Pinecone/Milvus/pgvector).
- Context Management: Efficiently managing token windows and costs.
- Guardrails: Ensuring your LLM integration doesn't leak private data.
Let’s Build
Have a complex backend challenge? Need to cut cloud costs or prepare your architecture for AI?