Enterprise Architect · AI Solutions Architect · hands-on
I design systems that ship — and stay on the tools through production.
The level where the system either holds under real enterprise constraints or quietly falls apart six months in. RAG that doesn't drift, agent pipelines that don't loop, an event-driven backbone that degrades gracefully next to legacy — with a defensible reason for every trade-off, in the language of the system, not frameworks.
What I design
AI systems for production
RAG done right — chunking, re-ranking, hybrid search, knowledge graphs over corporate data. Agentic pipelines orchestrated by success criteria, not hand-written prompt chains. Hallucination and context drift diagnosed at the root: the wrong level of abstraction, not a dumb model.
Event-driven backbones
The LLM as a consumer of an event stream — Kafka, webhooks, CI/CD, monitoring. Fan-out, saga, async AI-enrichment. Safe integration next to legacy, gradual replacement without a big-bang rewrite, business continuity preserved.
Governed for production
Human-in-the-loop checkpoints, audit trails, prompt-injection defense, data isolation, GDPR / EU AI Act. Predictable behavior under real constraints — not model magic.
Artifacts
The proof. Real production systems and readable code — not sandbox demos.
Event-driven backbone at enterprise scale
Askona · $680M+ revenue, 9,000+ employees: an event-driven architecture across 40+ services and 20+ teams; RAG over the corporate knowledge base; Kafka/RabbitMQ in a real production estate.
Public, verifiable code — NBO platform
ALS + LightGBM, Feast feature store, FastAPI/Celery, Prometheus/Jaeger observability, defined quality metrics and an A/B framework — readable end to end.
My own production AI system
RAG over a knowledge base, event-driven agents, a layered knowledge architecture. The patterns I design run my own operation.
ML in production at scale
PersonaClick: predictive analytics and recommendation models on 199M+ customer profiles; a legacy personalization platform rebuilt for streaming / incremental learning under real performance and data-quality constraints.
Under the hood
Stack: Anthropic Claude (API, MCP, tool use), Python, ElasticSearch, ClickHouse, SQL and vector stores, AWS / hybrid, Docker, IaC.
Built and run in production, not slideware. You can read the code, connect the MCP server, and open the running systems.
An architect who has also led
The architecture is grounded in delivery reality because I've owned it — CTO at UMI.CMS (SDLC turnaround: revenue +50% YoY, defects 5–10/day to under 5/week, releases from months to weekly), a department turnaround with full P&L at a large system integrator, and enterprise-scale matrix leadership at Askona by architectural authority. For an architect role that's not a bid to manage — it's why the design accounts for org reality: staffing, politics, and what a team can actually run.
Let's talk.
Open to Enterprise Architect / AI Solutions Architect / System Architect roles — individual-contributor, hands-on.
Fully remote · EU-hours (CET/CEST) · open to relocating within the EU and working remotely from there (Blue Card–eligible) · EOR or contractor.
Still thinking it over? Here's a pile of links about me — look around first, write when you're ready.
- Personal site — profile, cases, how I position the work
- Blog — writing on delivery, leadership, and applied AI
- Engineering Breakdowns — methods and failure modes from real systems
- LinkedIn — track record and recommendations
- AI Delivery — a live management-layer system I run
- Next Move Engine — the AI operating system behind how I work
- GitHub — open work and tools