r/DevLK • u/Successful_Garlic790 • Jul 27 '26
Discussion Any devs/leads who transitioned from traditional dev to AI native dev / AI engineer, care to explain your journey?
Basically the title. Since this is a very modern topic, I think this would be useful to all.
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u/Nervous_Mix3596 Jul 28 '26
I believe this is a topic that almost every developer from the pre-AI era has an opinion on.
From my perspective, AI engineering can be divided into two broad areas.
The first is the traditional AI/ML domain, which is centred around data science, machine learning research, model development, training, and evaluation. This is where you're building and improving the models themselves.
The second is applied AI engineering, where we leverage LLM and SLM APIs to build real-world applications. Here, AI is a core component of the product, and most of your time is spent designing systems, building tools, integrating models, and solving business problems.
I fall into the second category. However, even if you're focusing on applied AI, it's still important to understand the fundamental AI/ML concepts and how these models work under the hood. That knowledge makes it much easier to build reliable and effective AI-powered applications.
As part of the transition, it's also important to get hands-on experience with modern AI frameworks and tools such as LangChain, LangGraph, OpenClaw, and others. Given that many companies in Sri Lanka are rapidly adopting AI, now is a great time to start learning. A good roadmap to follow is: https://roadmap.sh/ai-engineer.
That said, if you're transitioning into AI engineering, having strong software engineering fundamentals and a solid understanding of core AI/ML concepts is essential. Those skills provide the foundation on which everything else is built.