Most of what you see online about “prompt engineering” (especially from course sellers) is just fancy wording and trial-and-error tricks to get ChatGPT to respond a certain way.
These prompts are often found by chance — and rarely used inside real companies.
No company hires someone just to “test prompts all day.” 😅
And most of those viral “mega prompts” don’t even work the same across different LLMs.
✅ So What ARE the Real LLM-Related Jobs?
1️⃣ Model-Focused Roles (High-level, research-heavy)
These are the people who work on the models, not just with them.
What they do:
- Train, fine-tune, and optimize LLMs
- Deploy large models at scale
- Build APIs and production systems
Skills you need:
- PyTorch, JAX, HuggingFace
- Deep understanding of model architectures
- System design & distributed computing
Typical companies:
OpenAI, Anthropic, Cohere, Google DeepMind, etc.
2️⃣ Application-Focused Roles (Most common)
These jobs are about building real-world products powered by LLMs.
Job titles:
1. Applied NLP Engineer
2. ML Engineer
3. Data Scientist
4. AI Application Developer
What they do:
- Build apps using LLMs
- Implement RAG pipelines
- Use tools like LangChain, LlamaIndex, Haystack
- Work with vector databases
- Design structured outputs
Yes, some prompt engineering — but just a small part of the job
🎯 Bottom Line
Prompt engineering alone won’t get you a job. But as one skill inside a broader LLM application development role, it absolutely matters. If you want to work in AI, focus on building stuff, not memorizing prompts.
