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dretureta/README.md

Hi there πŸ‘‹, I'm Denys Retureta

Senior Full-Stack Engineer & AI Engineer

Designing and shipping production-grade AI systems at scale

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πŸš€ About Me

Senior Full-Stack Engineer and AI Engineer with over a decade building distributed systems and, in recent years, productionizing Generative AI solutions.

I specialize in designing end-to-end architectures that combine robust backend engineering, cloud-native infrastructure, and advanced AI capabilities β€” turning experimental LLMs into reliable, secure, and scalable enterprise products.

Currently at Ceibal, I lead the architectural design and implementation of AI-infused platforms that serve national-scale educational impact.

  • 🌱 Deepening expertise in AI Agents orchestration, evaluative frameworks, and MLOps for LLMs
  • πŸ”­ Passionate about system design, homelabbing, and Open Source
  • πŸ’¬ Open to discussions on RAG at scale, multi-agent systems, and production AI reliability

πŸ› οΈ Tech Arsenal

Core Languages & Frameworks

Languages & Frameworks

Cloud, DevOps & Infrastructure

Cloud & DevOps

Databases & Observability

Databases & Observability

AI Engineering & Architecture

Frontier LLMs (GPT-5 series β€’ Claude 4 β€’ Gemini 3 β€’ Llama 4) β€’ Multi-Agent Systems β€’ RAG Pipelines at Scale β€’ Vector Databases β€’ LangChain / LlamaIndex β€’ Prompt Engineering & Evaluation β€’ Fine-tuning & Quantization β€’ Model Orchestration & Routing β€’ AI Observability & Cost Optimization


πŸ’Ό Experience

Senior Full-Stack Engineer | AI Engineer
Ceibal β€” Montevideo, Uruguay (Dec 2019 - Present)

  • Designed and led the architecture of Generative AI solutions using frontier models (GPT-5, Claude 4, Llama 4), implementing advanced RAG pipelines and autonomous agent systems that significantly reduced operational workload.
  • Engineered high-performance microservices and distributed architectures optimized for AI workloads, ensuring seamless integration with third-party platforms at national scale.
  • Architected intelligent process automation combining BPMS (Camunda) with AI decision engines, delivering 40% efficiency gains in critical workflows.
  • Led End-to-End delivery of multiple AI-infused products β€” from architectural design and requirements translation to secure, scalable, observable production deployments.
  • Established robust CI/CD, monitoring, and AI-specific observability pipelines (latency, token usage, hallucination detection, and model drift).

πŸ“« Let's Connect


"I don't just integrate LLMs β€” I architect complete, reliable, production-ready AI systems that deliver real business value."


Made with ❀️ and β˜• in Uruguay

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  1. mcp-konduct mcp-konduct Public

    Konduct lets you orchestrate multiple Model Context Protocol (MCP) servers through a single unified interface.

    TypeScript

  2. schoology-ts schoology-ts Public

    TypeScript client for the Schoology API with full type safety, OAuth 1.0a authentication, and automatic pagination.

    TypeScript