AI engineer is the role title that emerged when every product team needed someone who could wire up an LLM, ground it with retrieval, and keep it from hallucinating in front of customers. Your resume must show that you understand the gap between a notebook prompt and a production AI feature - latency budgets, token costs, evaluation frameworks, fallback behaviour when the model is wrong.
The strongest AI engineer resumes lead with shipped features and the metrics that proved they worked: deflection rate on support bots, accuracy on extraction tasks, p95 latency on streaming endpoints, $/conversation cost. Hiring managers at OpenAI, Anthropic, and AI-first startups also look for evidence you've built evaluation harnesses (golden sets, LLM-as-judge, regression suites) - not just prompted Claude in a Jupyter notebook.
ATS keywords to include: LLM, GPT-4, Claude, RAG, vector database (Pinecone, Weaviate, pgvector), LangChain or LlamaIndex, embeddings, prompt engineering, fine-tuning, function calling, structured outputs, evaluation, and the language and framework you ship in (Python + FastAPI is the modal stack).
This example works for
AI Engineer
Generative AI Engineer
Applied AI Engineer
LLM Engineer
AI Product Engineer
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