Every month, another company tells us they're stuck with a generative AI developer who's shipped something, but it's not yet production-grade. The project stalled in "pilot purgatory." Most of the time, the issue wasn't the developer's skill — it was that the company wasn't actually ready to hire one yet.
Before you start recruiting, run through this checklist to make sure your team and infrastructure can actually use a generative AI developer effectively.
# The AI Readiness Checklist
1. You Have a Specific Use Case, Not Just "AI"
If your answer is "we want to explore AI," you're not ready yet. If your answer is "we want a chatbot that answers customer support questions based on our documentation," you are. Generative AI developers build solutions for concrete problems. The more specific your use case, the more productive they'll be.
2. You've Defined the Success Criteria
What does "working" actually mean? Accuracy? Latency? Cost per query? How will you measure it? A generative AI developer needs to know whether they're optimizing for accuracy (fine-tuning, RAG, evaluation frameworks) or speed (simplified prompts, smaller models, caching). Vague goals stall projects.
3. You Have Data Ready, or at Least Identified
If your use case requires fine-tuning or RAG, you need data — documents, example Q&A pairs, or a database your system will search. If you haven't even located where your data lives, onboarding a generative AI developer will mean weeks of exploration before they can start meaningful work. Know where your data is.
4. Your Infrastructure Supports LLM Inference
Running an LLM requires compute — either cloud API credits (if you use GPT, Claude, Gemini) or GPU infrastructure (if you're self-hosting). Budget $100-1,000/month for small projects, potentially more at scale. A developer can't ship if you haven't budgeted for inference costs.
5. You've Identified Who Owns the Project
A generative AI project needs a product owner or project lead who sets priorities, clarifies requirements, and unblocks decisions. If the developer is working solo without clear direction, they'll thrash. Ensure there's a named person who owns this project's success.
6. You Have a Testing/Evaluation Process in Mind
How will you evaluate whether the model is working? Manual spot-checks? Automated benchmarks? A/B testing with users? The evaluation strategy shapes the entire build. Work out how you'll actually judge success before hiring.
7. Your Team Understands It's Not Magic
Generative AI is powerful but fallible. It hallucinates. It drifts as usage patterns change. It costs money. Ensure your stakeholders understand these limitations and are ready for iteration, not a one-shot solution.
8. You've Budgeted for Ongoing Maintenance
The developer ships the first version. Then you need to monitor it, retrain it, adjust prompts, handle edge cases. Budget for ongoing work or a small team, not a single sprint.
9. You Know What Model/Approach Fits Your Problem
Are you using a frontier API (GPT-4, Claude)? Fine-tuning? RAG? Each has different tradeoffs in cost, latency, and accuracy. A developer can advise, but if you understand which direction makes sense, onboarding is much faster.
10. You Have Buy-In From Leadership
AI projects have different risk profiles and timelines than traditional software. Ensure your leadership understands this and won't panic if the first month is exploration and iteration, not shipping features.
# If You Can't Check All These Boxes...
If you're checking "no" on 3+ of the above, you might be better off starting with a consultant or a short exploration engagement before hiring a dedicated generative AI developer. A consultant can help you clarify use case, requirements, and infrastructure, then hand off to a developer once things are concrete.
# If You Can Check All These Boxes...
You're ready. Hire your generative AI developer and set them up for success with clear priorities, identified data, and infrastructure in place. The difference between a stuck project and a shipped one is often just this level of clarity.
# Frequently Asked Questions
What if we don't have much data yet?
That's OK for API-only use cases (calling GPT or Claude with good prompts). For fine-tuning or RAG, data is critical. If you don't have it, that's a blocker worth fixing before hiring.
Should we hire a consultant first or jump straight to a developer?
If you're unclear on use case or infrastructure, a consultant makes sense. If you're clear but just need execution, hire the developer.
How long should onboarding take before a developer ships their first PR?
Usually 1-2 weeks of exploration if everything else is clear (use case, data, infrastructure). If you're still figuring these out, 3-4 weeks.
Can the developer figure out infrastructure if we haven't set it up?
Yes, but it burns their first month. Better to have cloud credits or GPU access ready to go.
What if we're not sure which model (fine-tuning vs RAG) we need?
That's a conversation to have before hiring, or scope the first sprint as exploration. A developer can advise, but you shouldn't have open-ended exploration be the whole project.
# Ready to Hire?
CompanyBench matches you with pre-vetted generative AI developers who can help you ship, not just explore. See the full breakdown on the Hire Generative AI Developer page.
"See generative AI developer profiles and rates at companybench.com/hire-talent/hire-generative-ai-developers. For fine-tuning and production LLM engineering specifically, see companybench.com/hire-talent/hire-llm-engineers — and for RAG-focused retrieval pipelines, see companybench.com/hire-talent/hire-rag-developers. For the full range of engagement models, start at companybench.com/hire-talent.
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