Artificial Intelligence & GenAI Resume Keywords: Complete ATS Reference
Artificial intelligence keywords have expanded rapidly as organizations integrate AI and generative AI into products and operations. ATS systems now distinguish between traditional ML, deep learning, and generative AI skills. Resume optimization for AI roles requires specificity about model architectures, training approaches, and deployment patterns rather than general AI claims.
Primary Keywords
Synonym Groups
ATS systems may recognize these variations. Use the canonical form when possible, but including synonyms ensures broader matching.
LLM
Also matches: large language model, foundation model, language model
RAG
Also matches: retrieval augmented generation, retrieval-augmented generation
GenAI
Also matches: generative AI, generative artificial intelligence
fine-tuning
Also matches: model fine-tuning, instruction tuning, RLHF
vector database
Also matches: vector store, vector search, Pinecone, Weaviate, Chroma
Related Skills
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Common Mistakes
- Listing 'AI experience' without specifying model types, architectures, or frameworks
- Not distinguishing between using AI tools (ChatGPT, Copilot) and building AI systems
- Omitting model evaluation metrics and responsible AI considerations
- Claiming LLM experience without specifying whether it involved prompting, fine-tuning, or pre-training
- Not naming specific model architectures: transformer, diffusion, GAN, VAE
Optimal Resume Placement
- Technical Skills section listing AI frameworks, model types, and platforms by name
- Experience bullets describing model architecture, training approach, and production impact
- Projects section for AI work with measurable outcomes and technical specifics
- Certifications section for AI/ML credentials (Google AI, AWS ML, DeepLearning.AI)