Integration Points
Uptime
Distributed Systems Architect
I architect distributed systems that hold under pressure — scalable, observable, and built to last.
7+ years shipping production backend systems — across enterprise platforms, logistics infrastructure, and e-commerce serving millions of users.
I specialize in distributed architectures — designing the boundaries, messaging patterns, and data consistency strategies that make microservices predictable at scale.
Currently exploring new opportunities — open to senior backend and distributed systems roles where scale, reliability, and AI infrastructure are the hard problems.
Not demos. Not tutorials. Systems that process thousands of transactions daily, serve millions of users, and maintain uptime SLAs under real pressure.
Integration Points
Uptime
API Endpoints
Shared Services
Platform Type
Oversell Rate
International B2C E-Commerce — DACH Market
Market
Marketplace
Market
Platform
Carrier Integrations
Architecture
ERP, CRM, WMS & Workflow Automation Suite — Odak Innovation
Web Platforms
Stack
Smart TV & Interactive Broadcast Platform for TV8
Standard
Streaming
Engineering principles are not rules — they are decisions made early that propagate through every layer of a system.
Horizontal by default, vertical when forced.
Systems that scale reactively always carry technical debt. Architecture decisions made at inception — data partitioning, stateless service boundaries, async communication patterns — determine whether a system bends or breaks under load.
Circuit breakers, not optimism.
Every distributed system experiences partial failure. The question is whether the failure is contained or cascading. Bulkheads, circuit breakers, and graceful degradation are the difference between an incident and a disaster.
Traces, metrics, logs — all three or none.
Observability is not a feature added post-launch — it is a design constraint from day one. Structured logs, distributed tracing, and RED metrics transform a black box into an understandable system.
The best architecture is the one you can explain.
Microservices, event sourcing, and CQRS are tools, not destinations. Each layer of abstraction must earn its place. A well-reasoned monolith beats a poorly understood service mesh every time.
Not using AI as a feature — integrating intelligence as infrastructure. Embedding pipelines, agent orchestration, and local inference as production concerns.
Retrieval-Augmented Generation systems connecting LLMs to proprietary knowledge bases. Embedding pipelines, vector store integrations, and context-window management for domain-specific applications.
Multi-agent systems where specialized agents handle distinct tasks coordinated by an orchestrator. Built for customer support automation and internal tooling with function calling and tool use.
On-premise LLM deployment using Ollama for air-gapped environments. Evaluated Mistral, LLaMA, and Phi families for domain-specific tasks with GGUF quantization strategies.
Designing .NET backend services that orchestrate AI workflows as first-class infrastructure. Streaming responses, async queuing for inference, and cost-aware routing between providers.
Retrieval-Augmented Generation: from user query to grounded LLM response.
Not just built to work — built to keep working when things go wrong. Event-driven order pipeline: how messages flow, retries happen, and consistency holds without tight coupling.
If you're building systems that need to survive production — distributed, resilient, and actually shipped — let's talk.