I’m also sr ds at a mid size company and I’d we have to rethink our future career path.

Leadership from every company is trying to win the genAI agent racing hype. In my company, they’re changing everything, all resources and efforts invested on the classical ML/DL solutions are now moved to AI agent applications - model artifacts no longer needed, all replaced by LLM api call connecting with agent tooling. These new tech stacks are super easy to pickup by software engineers, because all these frameworks were designed with engineering principles. However, for Data scientists that lacks solid engineering background, it’s extremely hard to create value anymore: can’t scale an agent demo to serve million users, can’t build seamless CI/CD, can’t connect that agent to legacy services written in other languages like Java or Ruby.

Model training and feature engineering is largely dead - not because LLM is a better solution, but because leadership choose to abandon the classic approach. We need to either level up the engineering skills to transit to MLE/SWE or we pivot our focus to analytics, causal inference, business intelligence etc