Your fine-tuning experiments don't belong in a generic software dev resume
Standard resume builders treat AI/ML work like generic programming projects.
The result? Your RAG pipeline optimization gets buried under "Software Developer" and your 40% inference speed improvement from quantization looks like any other performance metric.
Here's what we see missing from most technical resumes:
Project context gets lost
• "Built recommendation system" vs "Fine-tuned BERT for content recommendation, achieving 23% improvement in click-through rates"
• "Worked on data pipeline" vs "Implemented RAG architecture processing 10M+ documents with sub-200ms query response"
Technical depth disappears
• Generic bullet points that could describe any backend work
• No distinction between traditional ML and modern LLM methodologies
• Missing the specific frameworks, model architectures, and optimization techniques that matter
ATS systems fail
Standard parsers weren't designed for "LoRA fine-tuning" or "vector embeddings." They need proper structuring to recognize these as legitimate technical skills, not jargon.
The difference between "Python Developer" and "LLM Engineer" isn't just the title — it's how you present the technical complexity, the scale of data, and the measurable impact on model performance.
Your resume should immediately signal AI expertise, not generic software development.
What's the biggest gap you've seen between AI work and how it appears on resumes?
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