Get your ai engineer resume past ATS screening. Paste any job description below, get your keyword match score, and generate a tailored CV in 60 seconds.
These keywords appear most frequently in ai engineer job descriptions. Missing even a few can drop your ATS score below the screening threshold.
Hard and soft skills that ai engineer ATS systems look for
Common mistakes that cause ai engineer resumes to fail ATS screening
Include 'RAG' and 'Retrieval-Augmented Generation' as separate keywords - ATS may not expand acronyms
List specific LLM providers you've worked with: 'OpenAI GPT-4', 'Anthropic Claude 3', 'Google Gemini' - these are distinct ATS keywords
Add 'prompt engineering' and 'system prompt design' - despite debate about this term, it's a literal ATS keyword in thousands of 2024 JDs
Name your vector database explicitly: 'Pinecone', 'Weaviate', 'pgvector', 'Chroma' - ATS treats each as a separate filter
Include 'LLM evaluation' or 'evals' - production AI engineering roles now filter on evaluation and quality measurement experience
Add 'AI agents' and 'function calling' - agentic AI systems are the dominant trend in 2024 AI engineering JDs
An AI Engineer (emerging title 2023–2024) focuses primarily on integrating and productizing existing AI capabilities - especially LLMs - into applications. They use APIs, RAG pipelines, and prompt engineering rather than training models from scratch. ML Engineers focus on building, training, and optimizing models. AI engineer resumes should lead with LLM integration, RAG, LangChain, and product-level AI features. ML engineer resumes should lead with PyTorch, model training, and MLOps.
Describe the full stack: 'built RAG pipeline using LangChain, OpenAI embeddings, and Pinecone serving 50k queries/day with 92% relevance score', 'implemented hybrid search (BM25 + semantic) reducing hallucination rate by 40%'. Use all the keywords: RAG, vector database, embeddings, chunking strategy, retrieval, LangChain, LlamaIndex, Pinecone, semantic search, cosine similarity. Evaluation metrics (RAGAS scores, context recall) are strong differentiators.
Yes, despite the debate around it. 'Prompt engineering' appears as an explicit keyword in thousands of 2024 AI engineering JDs and ATS systems filter on it. More importantly, show what you did with it: 'designed system prompts achieving 94% task completion rate in production', 'reduced LLM API costs 60% through prompt optimization and output caching'. The keyword alone is weak; the keyword with metrics is compelling.
Pinecone is the most frequently mentioned in JDs, followed by Weaviate, Chroma, and Qdrant. pgvector is increasingly important for teams already using PostgreSQL. List all vector databases you've worked with as individual ATS keywords. Include the use case: 'used Pinecone for document search over 10M chunks with sub-100ms P95 latency'. Knowledge of indexing strategies (HNSW, IVF) and hybrid search is a strong differentiator for senior AI engineer roles.
Focus on what you've built with AI APIs, not on training models. Projects that resonate: 'built internal chatbot using GPT-4 + RAG reducing support tickets by 35%', 'created AI-powered code review tool using Claude API integrated into GitHub PRs'. List all AI-specific tools you've used: LangChain, OpenAI API, Pinecone, Hugging Face. Add a dedicated AI/LLM Projects section. The DeepLearning.AI short courses (free) add legitimate ATS keywords like 'LLMOps' and 'RAG' to your profile.
Guides to help you pass ATS screening faster