Designing and building scalable Data & Artificial Intelligence systems — from modern lakehouse architectures to production-grade AI platforms.

I focus on engineering-first solutions that combine data architecture, deep learning, LLM systems and quantitative AI models.

Production-oriented Artificial Intelligence systems across:

• Deep Learning (Python & C++)
• Computer Vision
• Generative AI & LLM Engineering
• AI-driven Financial Engineering
• Scalable ML System Design

🔗 Repository:
👉 https://github.com/douglasmitsue/ai-systems-engineering-portfolio

• Transformer & LLM implemented from scratch
• Retrieval-Augmented Generation (RAG) systems
• Real-time Computer Vision pipelines
• AI trading & quantitative finance models
• Distributed training simulations
• REST APIs & production deployment architectures

Built with a production-first mindset:

• Performance optimization
• Modular architecture
• Reproducible experiments
• System scalability
• Quantitative performance metrics

• Lakehouse design (Medallion architecture)
• Distributed data pipelines
• Scalable ETL/ELT systems
• Data modeling & performance optimization

• Deep Learning architectures
• Transformer internals
• LLM fine-tuning (LoRA, RAG)
• Computer Vision pipelines
• AI system deployment & monitoring

• Time-series forecasting
• Risk modeling
• Portfolio optimization
• AI-driven trading strategies

Python | C++ | PyTorch | Spark | Microsoft Fabric | Databricks | SQL | LangChain | Vector Databases | Docker | MLflow | Power BI

• Mathematics before abstraction
• Framework-aware, not framework-dependent
• Clean & scalable architecture
• Production-grade implementation
• Performance-driven optimization

Interested in building scalable AI platforms, modern data systems, and high-performance ML architectures.

📫 LinkedIn: https://linkedin.com/in/douglasmitsue
📂 GitHub: https://github.com/douglasmitsue