A strong AI engineer job description filters for production experience, not paper credentials. The template below is written for US companies hiring engineers who ship AI features that real users depend on, and every section can be pasted into your job board as-is or edited to match your stack.
Paste the template below into your job board or careers page, then replace the bracketed placeholders with your company's specifics. Every section is written to be edited, not just admired.
About the role [Company] is looking for an AI Engineer to own AI-powered features end to end, from scoping and prototyping through production deployment and monitoring. You will work directly with our product and engineering teams to decide where AI genuinely improves the customer experience, then build it to production standards. What you will do - Ship AI features into production, including model integration, retrieval pipelines, and evaluation. - Design guardrails and fallback behavior so features degrade gracefully when models fail. - Own inference cost and latency budgets, and report on them like any other production metric. - Pair with backend engineers to keep the AI layer well-tested and maintainable. - Stay current on the model landscape and recommend when to switch providers or approaches. What we are looking for - 3+ years of software engineering experience with at least one AI feature shipped to real users. - Strong Python plus one production backend language. - Practical experience with LLM APIs, embeddings, and RAG architectures. - An evaluation mindset: you measure quality instead of guessing. - Comfort working in a team that reviews code and writes tests. Nice to have - Fine-tuning experience, MLOps tooling background, or open-source contributions. Compensation and benefits Base salary of $130,000 to $200,000 depending on experience, plus [equity/bonus], health coverage, and a remote-friendly setup. Adjust this range to your market before posting. How to apply Send a resume and a short note describing one AI feature you shipped: what it did, how you evaluated it, and what broke in production. We respond to every application within [X] business days.
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Lead with the problems the engineer will own, then list production-focused requirements: shipped AI features, evaluation practices, and cost management. Avoid long tool checklists; the field moves too fast for them to age well.
US base salaries typically run from about $130,000 to $200,000 or more depending on seniority and specialty. Posting a realistic range improves application quality and is legally required in several states.
Only if you are training novel models. For product AI work, evidence of shipped features and sound evaluation habits predicts success far better than academic credentials.
Ask candidates to walk through one production AI feature they built: the architecture, the evaluation method, and a failure they handled. Depth in that conversation separates practitioners from tutorial followers quickly.
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