TGViewer
Data Engineers Data Engineers @sql_engineer · 11.2K subscribers
Post #936 3.95K
Prompt Engineering in itself does not warrant a separate job.

Most of the things you see online related to prompts (especially things said by people selling courses) is mostly just writing some crazy text to get ChatGPT to do some specific task. Most of these prompts are just been found by serendipity and are never used in any company. They may be fine for personal usage but no company is going to pay a person to try out prompts 😅. Also a lot of these prompts don't work for any other LLMs apart from ChatGPT.

You have mostly two types of jobs in this field nowadays, one is more focused on training, optimizing and deploying models. For this knowing the architecture of LLMs is critical and a strong background in PyTorch, Jax and HuggingFace is required. Other engineering skills like System Design and building APIs is also important for some jobs. This is the work you would find in companies like OpenAI, Anthropic, Cohere etc.

The other is jobs where you build applications using LLMs (this comprises of majority of the companies that do LLM related work nowadays, both product based and service based). Roles in these companies are called Applied NLP Engineer or ML Engineer, sometimes even Data Scientist roles. For this you mostly need to understand how LLMs can be used for different applications as well as know the necessary frameworks for building LLM applications (Langchain/LlamaIndex/Haystack). Apart from this, you need to know LLM specific techniques for applications like Vector Search, RAG, Structured Text Generation. This is also where some part of your role involves prompt engineering. Its not the most crucial bit, but it is important in some cases, especially when you are limited in the other techniques.
  • ❤ 4
  • 👏 2
More from @sql_engineer
  1. Aug 29, 2026Example: Source Database → CDC → Only Changed Records → Data Platform CDC is especially us…
  2. Aug 29, 2026🚀 Data Engineering Fundamentals – Part 7 📥 Data Ingestion: How Data Enters a Data Platfo…
  3. Aug 18, 2026🚀 Data Engineering Fundamentals – Part 6 📌 ETL vs ELT: How Data Moves from Source to Des…
  4. Aug 11, 2026📊 The 90-Minutes Business Analytics Masterclass Learn how to transform raw data into powe…
  5. Aug 8, 2026Data Warehouse Stores: Cleaned sales data Customer KPIs Revenue reports Historical busines…
  6. Aug 8, 2026🚀 Data Engineering Fundamentals – Part 4 📌 Databases vs Data Warehouses vs Data Lakes vs…
Threads Profile ViewerView any public Threads profile without an account.Open ThreadLook →Writing with AI? Make it sound human.Metric37 rewrites AI drafts so they read naturally. Free AI detector, 1,500 words free.Try Metric37 →