Engineer
Senior ML Engineer | GenAI + RAG Systems | Fine-tuning | MLOps | Conversational & Document AI Building reliable, real-time AI systems across high-impact domains — from Conversational AI and Document Intelligence to Healthcare, Retail, and Compliance. At 7-Eleven, I lead GenAI initiatives involving LLM fine-tuning (Mistral, QLoRA, Unsloth), hybrid RAG pipelines, and multimodal agent-based bots. Domains I specialize in: Conversational AI (Teams + Claude bots, product QA agents) Document AI (OCR + RAG, contract Q&A, layout parsing) Retail & CPG (vendor mapping, shelf audits, promotion lift) Healthcare AI (clinical retrieval, Mayo Clinic work) MLOps & Infra (Databricks, MLflow, vector DBs, CI/CD) Multimodal Vision+LLM (part lookup from images) I work at the intersection of LLM performance, retrieval relevance, and scalable deployment — making AI not just smart, but production-ready. Let’s connect if you’re exploring RAG architectures, chatbot infra, or fine-tuning strategy!
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Starting deterministic is advice I wish I had taken sooner: a rules-based router is far easier to debug than a model deciding every hop. I only reach for an LLM router when the branching genuinely cannot be enumerated. Where did you draw the line between deterministic routing and letting the model decide?
Ahmet Özel
Start deterministic is the advice I would give too, and the reason is debuggability rather than accuracy. A deterministic router that is wrong 10% of the time is wrong in ways you can enumerate and fix; an LLM router wrong 5% of the time is wrong differently each run, so you never accumulate knowledge about the failure.
From the document-AI side there is a free benefit too: a rule-based router produces labelled data as a byproduct, so when you do want a learned router later you have a training set drawn from real traffic rather than guesses.
Worth stating the reverse condition as well. The honest trigger for going non-deterministic is when the rule set stops being enumerable, not when someone wants it to feel smarter.