Hire · carbon.talent
Hire a contract AI / LLM engineer
Last updated 18 Oct 2026
Hire a contract AI / LLM engineer when you need a scoped path from prototype to production: evaluation, serving and monitoring for a defined term. Post the role free on carbon.talent, review vetted profiles and agree the rate on the platform.
For longer ownership see fractional AI / ML or full-time.
What this contract role owns
- A scoped path from prototype to production
- Evaluation, serving and monitoring for the term
- Handover so your team can run and extend the feature
Contract vs other engagements
| Question | Contract | Alternative |
|---|---|---|
| Shape | Fixed-term build | Ongoing full-time ownership |
| Best for | First production AI feature | Core AI team |
What we look for
- Production experience with evaluation, not only demos or notebooks.
- Clear ownership of failure modes, rollback and monitoring.
- Judgment on build vs buy for models, prompts and retrieval.
On carbon.talent
- Vetting report from the real-world data experience test (scenario-based).
- Working-style profile and short video intro.
- ID checks available on Team and Scale. Team plan: 30 days free, no card needed.
How to hire on carbon.talent
- Post your role free as contract.
- Review applicants ranked by fit.
- Compare vetted profiles against your brief.
- Agree the rate and sign on the platform.
Questions companies ask
- When should I hire a contract AI / LLM engineer?
- When you have a defined production milestone (first feature, eval harness or serving path) and need someone who has shipped before, for a fixed term.
- What should the AI / LLM contract brief include?
- The use case, success criteria, data access, latency or cost limits, and who owns the feature after handover.
- How do I hire a contract AI / LLM engineer on carbon.talent?
- Post the role free as contract, review vetted applicants ranked by fit, then agree the rate and contract on the platform.
- Contract AI / LLM vs machine learning engineer?
- AI / LLM work often centres on evaluation, serving and product integration. ML engineers more often own training pipelines and classical models. Name the outcome you need.