You hire an AI developer to make a model-based feature reliable, not just to get it working once. That is what we vet for, so the engineers we place can own a retrieval pipeline, an agent or an LLM integration through to production. Starting is simple: you tell us the work, and a match follows after a short intro call. For the project version of the same skills, see AI development services.
What do we vet AI engineers for?
Plenty of candidates can call an API. We look for the skills that decide whether the result holds up.
- Evaluation: Can they build a test set from real cases, pick metrics that match the task, and use the results to decide between prompts, models and settings? This is the first thing we probe, because without it every change is a guess.
- Retrieval quality: Chunking, embeddings, hybrid search, reranking, and how to diagnose a wrong answer as a retrieval miss rather than a model failure. See our RAG guide.
- Prompt and tool design: Clear instructions, structured output, narrow tools with validated inputs, and sensible refusal behavior.
- Python and TypeScript: Python for data and model work, TypeScript for the product code around it. Most of our AI work touches both.
- MLOps basics: Versioning prompts and models, logging cost and latency, monitoring for regressions, and deploying a model change like any other release.
- Judgment: Knowing when a rule or a search index beats a model, and saying so.
We screen with a practical task on a small dataset rather than trivia, and we read how the candidate reasons about failures.
What do they do in the first weeks?
Typical first tasks, depending on where your project stands:
- Build the evaluation set from your real data and score what you have today.
- Find the top failure causes in an existing prototype, and fix retrieval or prompts against the set.
- Add logging, cost tracking and a quality dashboard so the team can see what the system does.
- Wrap a model call behind an interface, with validation and fallbacks.
- Move a notebook prototype into a service with tests and deployment.
If you are earlier than that, the engineer can run the use case and data check described in our AI consulting work.
Which engagement model fits?
| Model | Fits when | More |
|---|---|---|
| Dedicated AI developer | You have a roadmap and need one senior person inside your team | Works in your tools and standups |
| Team extension | You need a small group for an AI product line | See IT staff augmentation |
| Dedicated team | You want a stable team that owns an AI product | See hire a dedicated team |
| Fixed-scope project | The outcome is clear and you prefer a deliverable | See AI development services |
Rates for developers are in the range of $50 to $150 per hour, depending on seniority and the mix of skills. A short intro call settles the role, the stack and the level you need.
What proof do we have?
We have built retrieval assistants, forecasting models, document pipelines and computer vision systems. For a US-based Series-B legal-research SaaS, the retrieval pipeline reached precision above 85% on the client’s own evaluation set, with answer latency down by about 60%; see the case study. For a US-based Series-B fintech, an OCR pipeline cut manual review time by about 70%; see the OCR case study. Those figures are directional, from the clients’ own testing.
How do you start?
Tell us the product, the stack and what the engineer would own. After a short intro call we propose candidates whose experience matches, and you interview them yourself. For work with agents specifically, read about AI agent development, and to begin the conversation, contact us.
