u/dallsilre

saw a post about the Amazon AI language engineer role and it got me thinking about the broader picture. from what I can tell, a lot of language engineering work has shifted pretty heavily toward, LLM-based stuff - RAG pipelines, agent workflows, fine-tuning smaller models for specific domains, that kind of thing. makes sense given how fast adoption has moved. curious whether people in this space feel like traditional NLP skills (parsing, morphology, the more linguistic, side) still matter much day-to-day, or if it's mostly just prompt engineering and orchestration frameworks now. and for anyone who's made the jump from more classical NLP roles into LLM-heavy work, was the transition pretty smooth or did it require a big re-skill?

been burned a couple times now so thought I'd share what I've picked up. the biggest one for me is when someone can't show you actual results from actual clients. not vibes, not follower counts, not a certificate from a 6 week course. real numbers from real campaigns. the second someone starts leading with buzzwords and vague promises instead of showing you what they've done, I'm out. also watch for people who only know one channel and pitch themselves as a full-stack marketer. SEO-only or social-only is fine if that's what you need, but if they're claiming to do everything and can't explain how the channels connect, that's a problem. the other thing that catches people out is hiring someone who just executes whatever you tell them rather than pushing back with actual strategy. a decent marketer should disagree with you sometimes. if they're just nodding along to everything and never questioning your brief, they probably don't know enough to have an opinion. curious whether others have found the 'no portfolio, but heaps of confidence' type to be the most common issue or if there's something else you keep running into.