From Non-CS to Full-Stack AI Engineer: My Learning in Public Roadmap

Arham Ghori — Tue, 25 Nov 2025 13:03:22 +0000

👋 Introduction: The Start of a Long Journey

I’m starting something long-term, challenging, and ambitious: building the skills to become a Full-Stack Web Developer and AI Engineer from the ground up.

My background isn’t in computer science—I completed 12th-grade PCB (Physics, Chemistry, Biology)—so this is a complete transition into software development. I’m documenting the entire process publicly to create accountability, measure my progress, and eventually build a transparent, verifiable portfolio of work.

This is not a fast-track attempt or a pursuit of shortcuts. It’s a structured, multi-phase journey based on a detailed set of roadmaps. The goal is to understand both the fundamentals and the practical engineering skills required to build real AI-powered applications.

📢 Why I’m Learning in Public

My motivations are simple and practical:

Accountability: Publishing my progress forces me to stay consistent and disciplined over months, not days.
Clarity: Writing publicly helps me understand what I’m learning and why.
Transparency: I want a portfolio that doesn’t just show final projects but the process behind them: the trade-offs, mistakes, and decisions.
Community: By building openly, I can learn from others who have taken similar paths.

"This isn’t about building a personal brand. It’s about documenting reality—slow improvement over time, grounded in structured practice and honest reflection."

🗺️ The Road Ahead: My Learning Blueprint

My roadmap breaks the journey into clear, progressive phases. Each phase builds on the previous one and ends with a small but functional project to ensure the concepts are not just theoretical.

Phase 0: Foundations 🏗️

Before writing production code, I need underlying mental tools. This phase sets the base for both programming and future AI reasoning.

Logic and computational thinking
Basic statistics
Foundational linear algebra concepts
The core ideas behind how software works

Phase 1: Programming Fundamentals (Python) 🐍

Python is my starting point because it's widely used in backend development, automation, and AI systems.

Syntax and control flow
Data structures & error handling
Writing clean, readable code
Testing fundamentals
Version control with Git

Phase 2: Frontend Development 🎨

To build end-to-end applications, I need to understand how users interact with software.

HTML structure
CSS layouts and responsive design
Modern JavaScript
React and eventually Next.js

Phase 3: Backend Development & Databases ⚙️

With frontend basics established, I’ll learn how real applications operate behind the scenes. This is where engineering habits form: structure, clarity, reliability, and maintainability.

APIs and backend architecture (FastAPI or Express.js)
Authentication flows
Relational and NoSQL databases (PostgreSQL, MongoDB)
Docker and containerization basics

Phase 4: Full-Stack Integration & Deployment 🚀

I’ll connect frontend, backend, and databases into functional systems.

API consumption & user flows
CI/CD fundamentals
Deploying full-stack apps

Phase 5: AI & LLM Engineering 🤖

Once the full-stack foundation is solid, I’ll move into modern AI engineering. I’m focusing on integration and building real features with existing tools.

Prompt design principles
Embedding models & Vector databases
Retrieval-Augmented Generation (RAG)
Evaluating AI system performance
Managing latency, cost, and context windows

Phase 6: Capstone Projects 🏆

Using everything above, I’ll design and build 3–5 full-stack AI-powered projects (e.g., a personalized RAG chatbot, workflow assistants). Each project will include a write-up explaining engineering choices, trade-offs, and limitations.

Phase 7: Portfolio & Career Prep 💼

Refining the portfolio, building case studies, and preparing for opportunities that match the skills proven through the projects.

📝 How I Will Document Everything

This is a learning-in-public journey with structured reporting. Here is what you can expect:

Weekly Summaries: What I learned, what I built, what I struggled with, and what I improved.
Public Code Repositories: Every phase will have its own folder, projects, and documentation.
Deep Dives: Reflections on debugging issues, architecture decisions, and system diagrams.

My Starting Point (Week 1)

I’m currently beginning with:

Setting up my development environment.
Creating a structured learning log.
Starting Phase 0 (Logic, Stats).
Practicing Python fundamentals daily.

Closing Thoughts

This longform introduction marks the beginning of a disciplined, transparent, and technically grounded learning journey. I expect setbacks, slow progress at times, and difficult concepts—but I also expect steady improvement through deliberate practice.

If you’re on a similar path or have experience in full-stack or AI engineering, I’m always open to learning from others. But above all, this process is about accountability.

More updates to come.

— Arham Ghori

DEV Community: Arham Ghori