Resume gap analysis for AI Engineer

Prompt engineering depth is stated, never evidenced

Most AI 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 on eval set

Hiring managers for this role scan for answer accuracy on eval set and token cost per request. A resume without those figures reads as an ai 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.

LLM integration and RAG pipelines buried under duties

LLM integration and RAG pipelines are core screening keywords for AI 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 LLM-powered features and retrieval pipelines" 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 Vector databases experience.

Certifications and qualifications not surfaced

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

Responsibilities