Freelance AI / LLM Engineers
Hire a freelance LLM engineer who has already shipped one
Super Cat Technology rents senior AI and LLM engineers who embed in your team on a time-and-materials basis. You get a named engineer who has taken retrieval systems, agents and fine-tuned models to production, with a firm accountable behind them.
Not for a one-off prompt or a weekend prototype; a marketplace gig suits that better. Not for a permanent hire either: we rent people, we don't recruit them.
Production LLM work you can check
Plenty of engineers can demo a chatbot. Fewer have kept one running for real users. These are LLM systems our team built and shipped. Need retrieval over Hong Kong documents in particular? See our enterprise RAG work in Hong Kong.
On-premise retrieval for a Big 4 firm
Phi-3, Llama and Jina models tuned on the firm’s own H100 cluster. Every answer comes back with its source paragraph.
Natural-language EDI
A Llama 3 model, fine-tuned with SFT and ORPO on X12 messages, answers business questions, with SAP IDoc matching.
AI voice agents at scale
200k calls a month for a 5,000-employee US enterprise. They book 67% of calls, against 77% for human agents.
A Slack assistant over Confluence
Answers from the company’s own wiki, with cited answers and honest refusals.
A commercial voice product
Built over 247 hours across three months and rated 5.0 by the client.
A marketplace freelancer, or an engineer who embeds
| Marketplace freelancer | Embedded engineer from Super Cat | |
|---|---|---|
| Who you get | A profile you pick from a large pool | A named senior engineer matched to the work |
| Proof | Ratings and a portfolio you assess yourself | Production systems you can ask about, and a founder with a verified Upwork record |
| Where the work happens | Often the freelancer’s own setup | Your repositories, behind your security review |
| Accountable | The individual | A firm, with Cat Yung as the named lead |
| If you need more people | Start a new search | Add engineers to the same team |
Comparing providers by name? Our Toptal alternative page sets the options side by side. Need a data scientist rather than an LLM engineer? See rent a data scientist.
What you provide, and what the engineer does
| The engineer does | You provide |
|---|---|
| Maps the workflow, the data and the constraints | One owner who can answer questions and decide |
| Prototypes the riskiest part first | Access to the systems and a real data sample |
| Builds in your repositories, with an evaluation suite from week one | Accounts, repository access and your security review |
| Tests on your real cases and reports the numbers as they are | The people who will use the system day to day |
| Runs the system for you, or hands over code, evaluation suite and runbook | A decision on who owns it after go-live |
How an engagement runs
Discovery
We map the workflow, data and constraints, and prototype the riskiest part. Then we score each candidate use case by value, effort and risk.
Embedded delivery
The engineer ships in your repositories, behind your security review. An evaluation suite runs from week one.
Run it, or own it
An optional monthly retainer to operate and monitor the system. Or a handover of the code, the evaluation suite and the runbook.
Risks we raise early
The data is not what it looked like.
Records can be thinner or messier than the first call suggested. We check a real sample in the first stage and tell you what it supports.
A model or vendor changes under us.
Providers retire models and change data terms. We check vendor terms at selection and re-route to an alternative when terms change. Your legal or compliance team reviews them too.
Access and security take longer.
We list access needs in the first stage and raise blockers the day they appear. You start the security review as soon as scope is agreed.
Results in testing differ from results in use.
We measure on your real cases, report the numbers as they are, and scope fixes. You run the pilot with the people who will use it.
Who stands behind the engineer
Every engagement is led by Cat Yung, Founder of Super Cat Technology. She is Expert-Vetted on Upwork, top 1% platform-wide, with a 100% Job Success Score (source: Upwork profile, October 2026), across 21 AI agent, RAG and LLM engagements. She has over a decade in production AI, NLP and ML.
The badge is awarded by Upwork after its own review, not self-reported. Our guide on how to verify an AI engineer's credentials shows how to check it in ten minutes.
Questions buyers ask
How do I hire a freelance LLM engineer with production experience?
Ask for a production system you can question, not a demo. A good freelance LLM engineer can explain how their system was evaluated, what broke after launch and how retrieval was tested. Super Cat Technology, as of October 2026, rents engineers who have shipped retrieval, agents and fine-tuned models, embedded in your team on a time-and-materials basis.
Freelance LLM engineer or an embedded team: which do I need?
One engineer suits a defined build with a clear owner on your side. A small embedded team suits work that spans data, retrieval, evaluation and integration at once. Super Cat Technology starts with one engineer or a team and adds people as the work grows, without new recruitment.
What should a freelance AI engineer have shipped?
At least one LLM system that real users rely on, with evaluation and monitoring. Ask how answers are checked against sources, how a model change would be handled, and what the system refuses to answer. Super Cat Technology’s engineers have shipped on-premise retrieval, voice agents and fine-tuned Llama 3 models in production.
Can a freelance LLM engineer work inside our security review?
Yes, and they should. Super Cat Technology’s engineers work in the client’s repositories, behind the client’s security review, with access listed in the first stage. Where documents must stay in-house, the system can be built on your own infrastructure, as our team did for a Big 4 firm’s on-premise retrieval system.
Bring in an LLM engineer who has done it before.
Tell us what you want the LLM system to do and where your data lives. On the first call we say whether one engineer or a small team fits, and what we would prototype first.