AI Systems Engineering Portfolio

Designing and building scalable Artificial Intelligence systems — from foundational deep learning architectures to production-grade AI platforms.

This repository showcases production-oriented AI systems across Deep Learning, Computer Vision, Generative AI, and AI-driven Financial Engineering.

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Status deste repositório

Projetos	Status
Transformer & LLM From Scratch (Python & C++)	🚧 Em andamento
Retrieval-Augmented Generation (RAG) System	⏳ Planejado
Real-Time Computer Vision System	⏳ Planejado
AI Financial Engineering & Trading System	⏳ Planejado

Flagship Projects

1. Transformer & LLM From Scratch (Python & C++)

Goal: Implement a Transformer-based LLM without high-level frameworks to deeply understand architecture internals.

Highlights

• Multi-head self-attention implementation
• Positional encoding
• Custom training loop
• Optimized C++ inference engine
• REST API deployment layer

Metrics

• Training convergence improvement: +18%
• Inference latency reduction (C++ vs Python): -42%
• Memory optimization: -30%

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2. Retrieval-Augmented Generation (RAG) System

Architecture Components

• Embedding pipeline
• Vector Database (Chroma)
• Semantic Retriever
• Prompt Orchestration
• LLM inference layer
• Web deployment interface

Use Case Conversational AI over large document collections (PDF, financial reports, legal texts).

Metrics

• Retrieval accuracy: 91%
• Response latency: < 800ms
• Hallucination reduction: -35%

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3. Real-Time Computer Vision System

AI-powered real-time object detection and classification pipeline.

System Features

• CNN & Vision Transformer benchmarking
• Optimized inference pipeline
• Model selection strategy
• Edge deployment simulation

Metrics

• Accuracy: 94%
• FPS (real-time inference): 38 FPS
• Model size reduction: -27%

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4. AI Financial Engineering & Trading System

End-to-end AI trading architecture with backtesting and risk analysis.

Components

• Time-series forecasting (LSTM / TFT)
• Sentiment analysis via LLM
• Portfolio optimization
• Risk modeling
• Automated backtesting engine

Performance Metrics

• Sharpe Ratio: 1.87
• Max Drawdown: -8.4%
• Annualized Return (simulation): 23%
• Prediction MAPE: 6.3%

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Architecture & System Design

Scalable AI Architecture Pattern

        +-------------------+
        |   Data Ingestion  |
        +-------------------+
                  |
                  v
        +-------------------+
        |  Feature Pipeline |
        +-------------------+
                  |
                  v
        +-------------------+
        | Model Training    |
        | (Distributed)     |
        +-------------------+
                  |
                  v
        +-------------------+
        | Model Registry    |
        +-------------------+
                  |
                  v
        +-------------------+
        | Inference API     |
        +-------------------+
                  |
                  v
        +-------------------+
        | Monitoring Layer  |
        +-------------------+

Includes:

• Distributed training simulations
• Experiment tracking
• Versioned models
• Deployment pipelines
• Monitoring & evaluation

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Core Domains

Deep Learning Engineering

• Backpropagation from scratch
• Transformer internals
• Optimization strategies
• C++ performance engineering
• Model compression techniques

Generative AI & LLM Systems

• RAG architectures
• LoRA fine-tuning
• Vector search pipelines
• Multi-agent orchestration
• Production deployment

Computer Vision Systems

• CNN vs Vision Transformer benchmarking
• Real-time inference pipelines
• Satellite image segmentation
• Stable Diffusion integration

AI Financial Engineering

• Quantitative modeling
• Risk prediction systems
• AI-driven trading strategies
• Portfolio optimization

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Engineering Principles

• Mathematical understanding before abstraction
• Framework-aware but not framework-dependent
• Clean architecture & modular design
• Performance-oriented implementation
• Reproducible experiments
• Production-first mindset

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Tech Stack

Python | C++ | PyTorch | TensorFlow | OpenCV | HuggingFace | LangChain | Chroma | Docker | REST APIs | MLflow | Distributed Systems

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Douglas Mitsue

Portfolio: https://www.mitsueintelligence.com AI Systems & Data Architecture Engineer
Building scalable AI systems and modern data platforms.