Resume gap analysis for LLM Engineer

Prompt engineering depth is stated, never evidenced

Most LLM Engineer resumes list Prompt engineering as a bullet in a skills bar. Job descriptions ask what you built with it. Attach Prompt engineering to one project, its scope, and the result.

No numbers against answer accuracy

Hiring managers for this role scan for answer accuracy and latency per request. A resume without those figures reads as a llm engineer who was present, not one who moved anything.

OpenAI API listed, LangChain missing

JDs for this role usually pair OpenAI API with LangChain. Naming only one signals partial coverage of the workflow and drops your keyword match.

RAG pipelines and Vector databases buried under duties

RAG pipelines and Vector databases are core screening keywords for LLM Engineer openings, but they often sit at the bottom of a paragraph. An ATS weights the first lines of each role far more heavily.

Scope of ownership is unclear

"build retrieval-augmented generation features for production" means something different at a 5-person team and a 500-person org. State team size, budget, volume, or user count so the reviewer can place your Fine-tuning experience.

Certifications and qualifications not surfaced

DeepLearning.AI certifications appear in the preferred section of most LLM Engineer JDs. If you hold one, it belongs near the top, not in a trailing "Others" line.

Responsibilities