AI Engineer CV example
Recruiters scan for shipped AI features, model/LLM work, and measurable impact — not just notebooks.
Sample professional summary
“AI engineer who shipped an LLM-powered support assistant cutting handle time 35%, with RAG, eval harnesses, and cost controls in production.”
Key skills recruiters look for
- Python
- PyTorch
- LLMs / RAG
- Vector DBs
- MLOps
- AWS/GCP
How to write strong bullet points
- Lead with shipped AI features and their business metric.
- Quantify accuracy, latency, and cost-per-request.
- Name models/frameworks only when relevant to the role.
AI Engineer career path & typical salary
How the role typically progresses, with the kind of responsibilities and approximate US base-salary range at each stage.
0–2 yrs
Builds prompts, RAG pipelines and evals on defined problems; integrates model APIs under guidance.
2–5 yrs
Owns AI features end to end: data, retrieval, evaluation, cost and latency in production.
5–8 yrs
Leads AI architecture for a product, sets eval and safety standards, and mentors.
8+ yrs
Drives AI strategy and the hardest modeling/serving problems across teams.
8+ yrs
Leads an AI team's roadmap, hiring and delivery; trades hands-on work for leadership.
Salary figures are approximate US market estimates for general guidance only. Actual pay varies widely by location, industry, employer, education and negotiation.
Common AI Engineer interview questions
Practice structured answers (situation, action, measurable result) — the same achievements belong on your CV.
- Walk me through an AI feature you shipped: data, retrieval, eval and cost.
- How do you evaluate an LLM feature when there's no clean ground truth?
- Your model is accurate offline but users complain — how do you debug it?
- How do you control latency and cost-per-request in production?
- When do you fine-tune vs. use RAG vs. prompt-engineer? Trade-offs?
- How do you guard against hallucination, prompt injection and data leakage?
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