Hire AI and ML engineers
Hire AI and ML engineers for the problem you actually have
AI and ML hiring covers eight distinct skill sets, from generative AI and RAG to data engineering and MLOps. Most US teams hire the wrong one because the titles blur together. This hub matches the engineer to the problem, then routes you to the specific role. Grape5 vets, dedicates, and backs each hire.
In short
AI and ML hiring covers eight distinct skill sets, from generative AI and RAG to data engineering and MLOps.
Most US teams hire the wrong one because the titles blur together. This hub matches the engineer to the problem, then routes you to the specific role. Grape5 vets, dedicates, and backs each hire.
Start with the outcome, not the buzzword. If you already have a model that works but keeps breaking in production, you need MLOps, not another data scientist. And if you are stitching together a repetitive workflow rather than building intelligence, AI automation is usually the faster, cheaper hire.
Which AI / ML role should you hire?
We’d hire
- Add generation to your product: chat, drafting, summaries, or images
- Generative AI engineer
- Ground answers in your own documents so the model stops making things up
- RAG engineer
- Have software plan and take multi-step actions, not just answer questions
- AI agents engineer
- Predict, score, forecast, or recommend from your own historical data
- ML engineer
- Read images, video, or scanned documents automatically
- Computer vision engineer
- Build the pipelines and clean data that every model depends on
- Data engineer
| We’d hire | |
|---|---|
| Add generation to your product: chat, drafting, summaries, or images | Generative AI engineer |
| Ground answers in your own documents so the model stops making things up | RAG engineer |
| Have software plan and take multi-step actions, not just answer questions | AI agents engineer |
| Predict, score, forecast, or recommend from your own historical data | ML engineer |
| Read images, video, or scanned documents automatically | Computer vision engineer |
| Build the pipelines and clean data that every model depends on | Data engineer |
Hire AI / ML developers by skill
From role spec to shipping, in five steps
- 01
Role spec → shortlist
You send the role and stack. We match from our vetted bench and shortlist people who have shipped it before.
- 02
Technical screen
A senior Grape5 engineer runs a live code and system-design screen, no take-home theater, no proxies.
- 03
Communication check
We check written and spoken English and how they reason out loud, the skills remote collaboration depends on.
- 04
You interview and decide
You meet the finalists and make the call. You hire the person, not a black box.
- 05
Onboard in your tools
They join your standups, repos and board in your timezone overlap, contributing in the first weeks, not the first quarter.
Frequently asked questions
An ML engineer trains and ships models from your own data for tasks like prediction and classification. A generative AI engineer builds on existing foundation models like GPT or Claude, using prompting, fine-tuning, RAG, and evaluation. If you are building on top of an LLM, you usually want the second one.
A data engineer builds the pipelines that move and clean your data. A data scientist finds patterns and decides what to model. An ML engineer puts models into production and keeps them running. Small teams usually need reliable data first, so the data engineer often comes before the rest.
A typical start is 2 to 3 weeks once the role is scoped. AI and ML matches can take a little longer because the vetting is stricter, and the same free replacement applies if the fit is wrong.
For a first AI feature, one strong engineer who knows prompting, RAG, and evaluation can carry it. Once you add agents that take real actions, or you need production monitoring and retraining, you are into MLOps and data engineering, which is a team. Start with one and scope up.
Senior Grape5 engineers run a live coding exercise, a system design conversation, and a communication check, so you are not just trusting a resume full of model names. We look for people who reason about tradeoffs and evaluation, not only API calls. Each hire is dedicated to your product and backed by a free replacement if the fit is wrong.
Build your AI / ML team in weeks
Tell us the roles you need, we’ll shortlist vetted, pre-vetted engineers and start in 2 to 3 weeks.