Python · SQL · Linux · Git
Software engineering habits that make AI work maintainable.
Journey / engineering path
From full-stack foundations to AI systems.
The through-line is practical: learn the minimum theory needed to build, debug, optimize and explain real systems.
Computer science + software foundations
Programming, databases, data structures, Linux, web development and security practice formed the base layer.
Applied ML + security automation
Projects moved into scikit-learn workflows, classification/forecasting, reconnaissance tooling and hands-on security labs.
GenAI systems
RAG, local LLMs, FastAPI, Ollama, prompt architecture and agent-oriented workflows became a central engineering focus.
Deep learning + production AI
Current work is moving through PyTorch and neural networks toward transformers, embeddings, fine-tuning, inference, evaluation and deployment.
Current stack
The engineering layers.
NumPy · Pandas · scikit-learn
Data preparation, modelling, evaluation and practical experimentation.
PyTorch · neural networks
Training loops, architectures, losses, optimizers and debugging.
Transformers · RAG · agents
LLM application architecture, retrieval, tool use and orchestration.
FastAPI · Docker · cloud
Serving, deployment, APIs and the infrastructure around models.
Nmap · Burp · Kali
Offensive-security practice used as a design and debugging lens.
Rule for the roadmap: if a topic does not improve my ability to build, debug, explain, optimize or deploy an AI system, it probably belongs later.