Resume Checker for AI Engineers

"AI experience" or "machine learning background" does not pass ATS filters at AI-first companies. Greenhouse and Ashby specifically score for LLM orchestration frameworks (LangChain, LlamaIndex), named vector databases (Pinecone, Chroma, Weaviate), and RAG architecture — as distinct, non-interchangeable keywords. Other high-frequency ATS failures: • No RAG context — Retrieval-Augmented Generation is the #1 demanded AI engineering pattern in 2026 JDs • Vector database not named — "vector search" alone fails most GenAI ATS filters • No evaluation framework mentioned — RAGAS, PromptFoo, or LLM-as-judge signals engineering maturity • Production deployment absent — "built a chatbot" vs "deployed AI agent serving 50K daily users at 99.9% uptime"

Our AI checks your resume against the job description for: • LLM frameworks: LangChain, LlamaIndex, LangGraph, AutoGen • Models: GPT-4o, Claude 3.5, Gemini, Llama 3, Mistral (each scored separately) • RAG stack: RAG, embeddings, vector database, Pinecone, Chroma, Weaviate, FAISS • Fine-tuning: LoRA, QLoRA, PEFT, HuggingFace, RLHF • Evaluation: RAGAS, PromptFoo, LLM evaluation, benchmark • Deployment: FastAPI, Docker, AWS SageMaker, model serving, MLOps • Optimization: token budget, caching, context window, latency optimization

1. Name the LLM stack explicitly — "LangChain + OpenAI GPT-4o + Pinecone" not "AI/ML tools" 2. Describe RAG architecture with metrics — "RAG pipeline achieving 87% answer accuracy on RAGAS benchmark" 3. Add production scale signals — "serving 50K daily users," "processing 500K embeddings," "99.9% uptime" 4. Include evaluation methodology — RAGAS, human evaluation, A/B testing of prompts 5. State cost optimisation if applicable — token reduction, caching, model selection are differentiators 6. Separate fine-tuning from prompting — LoRA/QLoRA fine-tuning is a distinct and highly valued keyword

The AI engineering role is evolving rapidly — 2026 JDs now routinely filter for agentic AI (multi-step reasoning, tool use, AI agents), evaluation infrastructure (not just building models), and production cost management at scale. Our ATS checker is updated to reflect current 2026 JD language across AI-first companies, enterprise AI teams, and consulting firms. Paste your JD to get an exact match score against the specific technologies and patterns that employer is hiring for.