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GuilhermeGors/README.md

Guilherme Oliveira

AI Engineer Β· Privacy-First RAG & Multi-Agent Systems Β· GDPR-Compliant Generative AI

Building enterprise AI infrastructure that runs locally, scales globally, and leaks nothing.

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🎯 What I Do & Impact

I engineer Generative AI platforms for highly-regulated environments, ensuring data privacy, scalable orchestration, and independence from cloud LLM providers. The market demands AI that respects GDPR β€” I build systems where infrastructure sovereignty is the default.

Instead of just prototyping, I approach AI Engineering with a results-driven mindset:

  • Sovereign Enterprise AI: Companies need AI without risking proprietary data leaks. Result: Architected systems like Codex One and IA News Agent, utilizing open-weights models (Ollama) and on-prem ChromaDB to achieve 100% data sovereignty and zero external API dependencies, making them audit-ready for strict compliance.
  • Workflow Automation at Scale: Manual data processing bottlenecks business growth. Result: Built JobMatch, a multi-agent RAG platform that parses unstructured resumes and performs semantic ranking, reducing human evaluation time by 95% while retaining precise skill-matching.
  • High-Throughput & Low-Latency AI: Cloud API costs scale linearly; local inference requires optimization. Result: Implemented batch prompting, asynchronous LangGraph orchestration, and exact/semantic dual-layer caching (achieving 12ms inference latency in Visual Tagger) to push CPU/GPU hardware to its limits.
Privacy-First RAG Pipelines    Β·    Multi-Agent Orchestration    Β·    Data Engineering (Spark + Scala)
Local LLM Deployment           Β·    Multi-Cloud Infrastructure   Β·    Security & GDPR Compliance

⚑ Featured Projects

πŸ“° IA News Agent

Autonomous multilingual intelligence pipeline

Aggregates, enriches, and validates AI research across 10 languages and 7 source types. Uses a dual-model LLM strategy (Qwen 3B + 14B) driven by a stateful LangGraph reasoning core. Complete on-prem execution with zero data leakage.

Python Ollama LangGraph ChromaDB FastAPI

πŸ”’ Codex One

Privacy-first enterprise knowledge base β€” fully offline RAG

Zero-egress architecture with pre-ingestion Threat Quarantine and automated PII Scrubbing. Uses CPU-bound ONNX embeddings and multilingual Cross-Encoder reranking (FlashRank) to isolate GPUs exclusively for Local LLMs (Ollama), delivering audit-ready compliance and high-performance retrieval.

Python FastAPI Next.js Ollama ChromaDB FlashRank

πŸ€– JobMatch

Multi-agent RAG platform for HR automation

Automated talent screening reducing manual evaluation by 95%. Semantic search over unstructured data, dynamic ranking, LLM-agnostic inference (OpenAI / Gemini / local). Real-time analytics via WebSockets.

FastAPI LangChain React WebSockets Vector DB

🏷️ Visual Tagger

High-performance AI image classification β€” 12ms latency

Concurrent ViT + CLIP Zero-Shot inference with confidence-score aggregation. Parallel model execution for automated multi-label tagging at production scale.

FastAPI PyTorch HuggingFace CLIP Docker

AI-augmented scalable sales management platform

Distributed high-performance ScyllaDB backend handling concurrent retail transactions. Features an embedded role-aware local LLM assistant (Ollama) providing contextual KPIs and mentorship without exposing metrics to third parties.

Node.js Express ScyllaDB Ollama Docker

Low-level engineering β€” C from scratch

Custom standard library, Unix process pipelines, 2D rendering engine. Built at 42 SΓ£o Paulo with zero external dependencies β€” raw memory management, file descriptors, and bitwise ops.

C Unix Makefile Memory Management


πŸ—οΈ Architecture β€” How I Build RAG Systems

flowchart TD
    %% Modern Premium Styling
    classDef gateway fill:#1E293B,stroke:#475569,stroke-width:2px,color:#F8FAFC,rx:8,ry:8
    classDef orchestrator fill:#334155,stroke:#64748B,stroke-width:2px,color:#F8FAFC,rx:8,ry:8
    classDef security fill:#991B1B,stroke:#EF4444,stroke-width:2px,color:#FEF2F2,rx:8,ry:8
    classDef pipeline fill:#0F766E,stroke:#14B8A6,stroke-width:2px,color:#F0FDFA,rx:8,ry:8
    classDef database fill:#4338CA,stroke:#6366F1,stroke-width:2px,color:#EEF2FF,rx:8,ry:8
    classDef inference fill:#6B21A8,stroke:#A855F7,stroke-width:2px,color:#FAF5FF,rx:8,ry:8

    subgraph Edge ["🌐 API & Gateway Layer"]
        direction LR
        Client(["Client Apps"])
        API["API Gateway<br/><i>(Auth & Rate Limits)</i>"]
    end

    subgraph Security ["πŸ›‘οΈ Enterprise Security"]
        direction TB
        Threat["Threat Scanner<br/><i>(Prompt Injection / Quarantine)</i>"]
        PII["Compliance Agent<br/><i>(PII Scrubber & Masking)</i>"]
    end

    subgraph Core ["πŸ”„ Two-Stage RAG Pipeline"]
        direction TB
        Router{"Semantic Router"}
        Embed["CPU Embeddings<br/><i>(FastEmbed / ONNX)</i>"]
        Retrieve["Initial Retrieval<br/><i>(Top-K Similarity)</i>"]
        Rerank["Cross-Encoder Reranker<br/><i>(FlashRank / High Precision)</i>"]
    end

    subgraph Storage ["πŸ’Ύ Data Sovereignty Layer"]
        direction LR
        Redis[("Redis<br/><i>(Session Memory)</i>")]
        Vector[("ChromaDB<br/><i>(Local Vector Store)</i>")]
    end

    subgraph Engine ["🧠 Local Inference Engine"]
        direction LR
        LLM("Open-Weights LLMs<br/><i>(Ollama / Zero Data Egress)</i>")
    end

    %% Flow Connections
    Client ===>|Requests| API
    API --->|Dispatch| Router
    
    %% Ingestion Flow
    Router -.->|Document Upload| Threat
    Threat -.->|Safe Data| PII
    PII -.->|Sanitized Text| Embed
    Embed -.->|Upsert Vectors| Vector

    %% Query Flow
    Router ===>|Query| Embed
    Embed ===>|Dense Vectors| Retrieve
    Retrieve <===>|Fetch Top 20| Vector
    Retrieve ===>|Pass Chunks| Rerank
    Rerank ===>|Top 5 Refined| LLM
    
    %% State
    Router <-->|Maintains Context| Redis

    %% Styling Application
    class Edge,Client,API gateway
    class Security,Threat,PII security
    class Core,Router,Embed,Retrieve,Rerank pipeline
    class Storage,Redis,Vector database
    class Engine,LLM inference

Loading

πŸ› οΈ Tech Stack

AI & ML

LangChain LlamaIndex Ollama HuggingFace PyTorch scikit-learn

Data Engineering

Spark Scala PostgreSQL MongoDB Redis ChromaDB

Cloud & DevOps

AWS GCP Azure Terraform Docker Ansible

Languages & APIs

Python C FastAPI React Node.js Linux


πŸ“ Core Engineering Core Principles

  • πŸ”’ Privacy & Law by Design: Engineered for environments where strict European GDPR dictates are mandatory. Implementing Offline LLMs and on-prem vector databases to guarantee zero third-party exposure.
  • πŸ”Œ Vendor-Agnostic Architecture: Building decoupled, LLM-agnostic platforms. Codebases designed such that swapping OpenAI ↔ Gemini ↔ Local Ollama requires zero restructuring.
  • πŸš€ Scalability & Resiliency: Designing hybrid architecturesβ€”from 200GB/day distributed Apache Spark systems to ultra-lightweight Dockerized microservices wrapped in circuit breakers and semantic cache layers.
  • βš™οΈ First-Principles Thinking: Grounded in the brutal 42 SΓ£o Paulo methodology (C, Unix, memory management). I don't just glue APIs together; I profoundly understand the performance parameters executing them.

πŸŽ“ Background

42 SΓ£o Paulo β€” Software Engineering (Peer-to-peer, zero-professor model)
Unisinos β€” Analysis & Systems Development
Bayswater College, London β€” Exchange Program

πŸ₯ˆ Moving The Cities β€” Unisinos Γ— SAP Γ— FH MΓΌnster (Germany) Γ— UAS7
πŸ† GeraΓ§Γ£o Caldeira β€” First Class, Institute Caldeira


Open solve problems
Fluent in English Β· Eligible anywhere :)

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