Transition from SE to AI Application Engineer

Thinking about moving into AI engineering but not sure where to start or whether your existing experience even counts? It does. The harder half of "AI engineering" is the engineering you already do; the AI part is a learnable layer on top.

In this 1-hour 1:1, we'll build a clear, realistic plan to get you from where you are now to building real AI-powered applications - tailored to your background, your stack, and your goals.

Who this is for:

Experienced software and backend developers (.NET, Java, Python, or any stack) who want to add AI engineering to what they already do without starting from scratch.

What we'll cover (you choose the focus):

1. A personalized roadmap based on your experience and where you want to go
2. The AI skills that actually matter at the application layer — RAG, agents, LLM integration — and what you can safely skip
3. How to leverage the engineering background you already have
4. Real project ideas to build a portfolio that proves your skills
5. The common time-wasters and pitfalls to avoid
6. Positioning and interview prep for AI engineering roles, if that's your aim

Why me:

I made this move myself, from a .NET background, and documented the full path — six stages, real projects, end to end. Now I help other engineers do the same, practically rather than theoretically.

You'll walk away with: clarity on your next steps, a concrete learning plan, and answers to your specific questions.

To get the most out of it: come with your background and one or two specific goals or questions — the more specific, the more useful the hour.

See the work behind this: the full From Software Engineer to AI Engineer series at https://codepth.dev/ai-path, and the projects I built along the way on GitHub: https://github.com/ashaniwale-codestack?tab=repositories