Systems that tell you whether something is achievable before it costs you anything.
I build applied AI systems that turn uncertain human workflows into structured, testable tools. My work sits between AI deployment, LLM evaluation, operations automation, and product prototyping. I am strongest where a team needs someone who can understand a messy real-world problem, model the logic, build a working system, and explain it clearly to technical and non-technical people.
Best fit: AI implementation, LLM evaluation, operations automation, technical product, or solutions engineering roles.
MILLE
Public benchmarkAn AI-assisted requirements system that turns a plain-language ML request and optional dataset into a structured, agent-ready implementation blueprint. Deterministic quality gates expose assumptions, broken specifications, invalid metrics, and acceptance criteria before code generation begins.
JENGA
Prototype / startup projectAppointment optimization for clinics and service businesses. Reduces no-show damage using reminders, cancellation windows, waitlist movement, and cascade rescheduling to recover empty slots — with an explainable risk score behind every decision.
PHYSIS
Working prototypeA hotel operations feasibility engine that models rooms, floors, cleaners, travel time, inspections, and maintenance blocks. It tests whether a hotel's daily plan is physically achievable before staff are assigned, using deterministic scheduling, CSV intake, validation, and what-if replanning.
AL-MIRAAH
Research prototype liveA five-tool MCP system that uses three-carrier layer-8 embeddings, a fixed 99-Name basis, centered cosine similarity, weighted Karcher means, and Poincaré distance to produce inspectable grounding profiles for classical Arabic terms. The geometry constrains semantic reasoning against this basis; it does not claim to decode one definitive meaning.
I'm looking for roles where I can help teams deploy practical AI systems, evaluate language models, automate internal workflows, or translate operational problems into software. The best fit is a hybrid role across AI implementation, solutions engineering, LLM evaluation, technical product, or operations automation.
AI Implementation
PHYSIS, Jenga, and MILLE show how I move from an operational problem to a tested system or working prototype; MILLE and AL-MIRAAH are available as live public tools.
LLM Evaluation
AL-MIRAAH — research-backed Arabic NLP grounding with explicit formulas, confidence signals, limitations, and reproducible evaluation.
Operations Automation
Hotel and healthcare workflow systems — scheduling, feasibility, and staffing logic.
Technical Product
Translating an idea into a usable demo, and explaining it to technical and non-technical people alike.
Across MILLE, Jenga, PHYSIS, and AL-MIRAAH, the same method repeats: take an uncertain, unstructured problem — a machine learning task, a booking calendar, a hotel floor plan, a body of classical Arabic text — and translate it into a structured system that can be tested rather than just trusted. That instinct has roots in classical Arabic philology, a discipline built around translating meaning precisely across forms: root to derivation, structure to sense. The engineering work applies the same instinct to operations and language models — turning a messy, human description of a problem into something that can be verified, and explaining the result back in plain terms.
Available for AI implementation, LLM evaluation, operations automation, and technical product roles in Vienna or remote Europe.