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Petru Arakiss

Engineering Reality > AI Hype

I build AI systems that survive contact with the real world. While others chase the latest models, I focus on the boring stuff that matters: reliability, observability, and making probabilistic software behave predictably at scale.

I lead AI engineering at Atlax 360. 19 years of building software taught me that the hard part isn't the prototype. It's making it reliable under load, bad data, and tight budgets. I spend most of my time on the unsexy stuff: error handling, monitoring, cost controls, and guardrails that prevent 3 AM incidents. Also run the frontend platform because good AI is pointless if nobody can use it.

REMOTE, EUOPEN TO CONSULTING
LATEST DISPATCH2025-11-21

Stop Optimizing Prompts. Optimize Context Instead.

Teams spend days tweaking system prompts while accuracy stays stuck. The real gains come from structuring context: feed the model the right data instead of better adjectives.

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Active Modules

Orchestrator

Multi-agent workflow engine with state persistence

Next.js, Python, Redis

EvalPipeline

Automated grading system for LLM outputs

LangChain, PyTorch

RAG-01

High-precision retrieval system with hybrid search

Qdrant, FastAPI

DocIntel

Structured data extraction from unstructured docs

OCR, Transformers
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