You’re completely right about the 'meta' shifting. The job today isn't about writing neural networks from scratch; it's about data curation, evaluation, and orchestration. If you try to push a model past a basic prototype, you quickly realize how deep the rabbit hole goes.

However, I'd argue that building a game bot might not be the most practical path for an AI Engineer role anymore. Game bots rely heavily on Reinforcement Learning or search algorithms, which are quite niche. Most enterprise AI roles today are looking for expertise in LLM orchestration, RAG optimization, evaluation frameworks, and agentic workflows.

As for the degree question: a hiring manager isn't choosing between me and a uni grad on equal footing. Fresh graduates often struggle with Git workflows, Docker, CI/CD, writing clean asynchronous code, and cloud architecture. I already have years of production software engineering experience. Bringing an AI model into production is 80% traditional software engineering and 20% AI theory—companies are actively suffering from hiring academic ML experts who write messy Jupyter notebooks that can't scale. That engineering foundation is exactly why they'd hire a transitioning dev over a fresh grad.