A template built for the roles that didn’t exist three years ago — structured to showcase LLM integration, RAG pipelines, model deployment, and the kind of applied AI work that companies are actually hiring for in 2026.
Tailor yours nowAI engineer with 3 years of experience building production LLM applications and ML infrastructure. Led development of a retrieval-augmented generation pipeline at Notion that powers AI-assisted writing for 4M+ users, reducing hallucination rates by 60% through custom embedding strategies and evaluation frameworks.
Languages: Python, TypeScript ML/AI: PyTorch, LangChain, Hugging Face, OpenAI API, RAG, RLHF, Fine-tuning Infrastructure: AWS SageMaker, Docker, Kubernetes, Pinecone, Weaviate Data: PostgreSQL, Redis, Apache Airflow
AI engineer job postings have exploded since 2023, but the role definition varies wildly between companies. Some want an ML researcher who can train models from scratch. Others want a software engineer who can integrate LLM APIs into production products. Your resume needs to signal which kind of AI engineer you are — and you do that through the specificity of your bullet points, not through a vague summary that says “passionate about AI.” If you built RAG pipelines, say RAG pipelines. If you fine-tuned models, name the framework and the scale.
The biggest shift in AI hiring in 2025–2026 is that companies care less about papers and more about whether you can ship. A bullet about deploying a model to production that serves real users at scale is worth more than a publication in a workshop. If you have both, great — but lead with the production work. Mention latency, throughput, cost optimization, error rates, and user-facing impact. These are the metrics that hiring managers at applied AI companies are scanning for.
Anyone can call an LLM API. What separates a strong AI engineer resume from a weak one is evidence that you know how to measure whether the AI actually works. Mention evaluation frameworks, benchmark scores, A/B test results, hallucination rates, faithfulness metrics. If you built custom evals for your use case, that’s a headline bullet. The industry is moving past “it kinda works” to “here’s how we know it works and how we catch when it doesn’t.”
Unlike traditional software engineering where “Python” and “PostgreSQL” are stable signals, the AI tooling landscape changes every six months. Listing LangChain, LlamaIndex, vLLM, Weights & Biases, Pinecone, or whatever you actually used tells a hiring manager exactly where you sit in the ecosystem. Being specific about your stack also helps with ATS keyword matching, since recruiters often search for specific framework names.
Include the ones you actually have. Leave out the ones you’d struggle to discuss in an interview.
For AI engineering roles, stick with the Classic template. The field is technical enough that your content does all the talking — you don’t need design flair to stand out. A clean serif layout lets hiring managers focus on what matters: your stack, your scale, and whether you’ve shipped AI to real users.
Use this templateTurquoise builds a tailored, ATS-friendly resume for any AI engineering role in minutes — whether you’re focused on LLM integration, model deployment, or applied ML. It rewrites your resume for the specific job using your real experience.
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