How to Hire an AI Engineer for Your Startup

The AI engineer market is loud right now. Everyone's an AI engineer. Most have built demos. Few have shipped production AI features that survive real users.

The single most important filter

Ask: "Show me a production AI feature you've shipped, with cost economics."

If they show you a demo, a Replit clone, or a "yeah I integrated GPT into our internal tool" — they haven't shipped real production AI.

If they show you: per-user cost dashboards, eval harness output, retry/fallback chains, prompt caching strategy, and the actual deployed feature — they're production-grade.

Five questions that separate signal from noise

1. "How do you prevent runaway costs in AI features?"

Good answer mentions: per-user rate limits, prompt caching, model-tier routing, cost dashboards, alerts. Bad answer: "We monitor it" or "we set a budget".

1. "How do you measure AI quality over time?"

Good answer: eval harness, golden test set, judge model, regression detection. Bad answer: "Users tell us" or "we test it manually".

1. "What happens when OpenAI has an outage?"

Good answer: provider abstraction, fallback chains, graceful degradation. Bad answer: "We use Azure" (still single-vendor risk).

1. "How do you keep RAG quality high as the corpus grows?"

Good answer: re-evaluation on changes, retrieval precision metrics, hybrid search, reranking. Bad answer: "We chunk it well".

1. "What's the largest AI feature you've shipped to paying users?"

You want specific numbers — daily active users, requests/day, cost/month, uptime.

Red flags

• Demo portfolios with no production references: AI engineers love to ship demos. Demos are fine. They're not the job.
• No cost awareness: AI features at scale can ruin gross margins. Engineers who haven't seen the bill don't know what they don't know.
• Single-provider thinking: Locked into OpenAI or Anthropic with no fallback strategy.
• No eval discipline: "We just try prompts and ship" is a quality time bomb.
• Resume packed with frameworks: LangChain experience matters less than knowing when not to use it.

What to skip

• Years of experience — irrelevant. Field is 3 years old. Look for projects shipped, not years served.
• Big-tech backgrounds — sometimes negative signal. Big-tech AI experience often skews to research or one-narrow-system.
• Open-source contributions — nice but not predictive of production skill.

What to actually pay for

• A senior engineer who has shipped a real AI feature to production
• Eval discipline — they think about quality measurement before shipping
• Cost discipline — they price your unit economics before launch
• Production-grade error handling — retries, fallbacks, circuit breakers
• Honest communication — they tell you when AI isn't the right answer

Solo engineer vs agency vs full-time hire

• Full-time hire if AI is your core competency and you have 12+ months of work. Annual cost: $200k–$400k loaded.
• AI engineering agency if you need a team and have $300k+ for the project. They're rare and expensive.
• Solo senior contractor if you have a defined scope (1–6 months) and want one expert end-to-end. Cost: $8k–$80k depending on scope.

My pitch

I run six AI products in production solo. Per-user cost dashboards, eval harnesses, retry/fallback chains — built into every integration I ship. Email [email protected].