AI Engineering

AI engineering is the direction I am actively developing on top of my data-science foundation. The focus is practical: understanding how modern AI systems are designed, integrated, evaluated, and deployed.

Areas I Am Building

• LLM application development
• AI agents and tool use
• Retrieval-augmented generation (RAG)
• APIs, model integration, and orchestration
• Evaluation, testing, and reliability
• Production-oriented AI engineering patterns

Why It Fits My Background

AI engineering builds naturally on the skills I already use: data preparation, statistical reasoning, Python, software workflows, testing, deployment, and communicating technical results. My goal is to combine those foundations with modern AI system design.

Building in Public

This section will grow as I build and document hands-on AI engineering projects. The emphasis will be on what I actually build, how it works, how I evaluate it, and what I learn from the process.