A fine tuned model
An open weight model adapted to your task using a parameter efficient method, with the training data, configuration and resulting weights kept as a reproducible artefact.
AI and Machine Learning
Hire an LLM engineer in Dubai
Fine tuning, self hosting, evaluation and serving of large language models, for a business that needs more control than a hosted API alone provides.
- 4.7 Google rating
- 200+ clients
- In Dubai since 2018
45 minutesto a fixed written quote
A business tends to hire an LLM engineer in Dubai once a hosted API stops being enough on its own: the model needs to know something specific about your business that general training never taught it, request volume has grown to where API costs are a real budget line, or data residency rules mean the workload has to run somewhere you control. That work, underneath the application layer, is what an LLM engineer covers.
Day to day, the job splits into three connected pieces. Fine tuning adapts an existing open weight model to a specific task using your own data, most often through a parameter efficient technique. Serving takes that model, or a hosted one, and runs it reliably under real traffic. Evaluation ties the two together, proving with a real dataset that a change actually made the model better rather than just different, which is exactly what Hugging Face’s own Evaluate library is built to support in a repeatable way.
What this role builds
What an LLM engineer delivers
The infrastructure and process work you actually get when you hire an LLM engineer in Dubai, not just a single API integration.
A serving setup this LLM engineer builds
The model running behind an API of its own, on infrastructure sized for your expected traffic, whether that is a cloud GPU instance or a managed hosting platform.
An evaluation harness
A dataset of real examples with expected outcomes, run automatically against the model so a change is measured, not guessed at.
Latency and cost tuning
Batching, caching and quantisation work aimed at keeping response time and infrastructure spend under control as usage grows.
Guardrails around the model
Checks that catch an obviously wrong or unsafe output before it reaches a user, separate from the model itself.
A retraining or refresh plan
An agreed point at which the model is re evaluated against fresh data, so performance does not quietly drift as your business changes.
Skills that matter
What to check before you hire an LLM engineer
Infrastructure and evaluation skills, not only a familiarity with training scripts.
| Skill or tool | What good looks like | Why it matters |
|---|---|---|
| Parameter efficient fine tuning | Has actually run a technique such as LoRA on a real dataset, and can explain what it changed and why | Full retraining of a large model is rarely necessary and rarely a sensible use of budget |
| Model serving | Comfortable deploying a model behind an inference server built for the purpose, not a script running on one machine | A serving layer built for one demo user falls over under real traffic |
| Building an evaluation set | Has built a dataset from real task examples, with a clear way to score correctness, not just eyeballed a few outputs | Without an evaluation set, nobody can say whether a change helped |
| Quantisation and hardware awareness | Understands the trade off between model size, accuracy and the hardware available to run it | The wrong hardware choice can make a self hosted model slower and more expensive than the API it was meant to replace |
| Licence awareness | Checks the specific licence of any open weight model before it goes into production | Open weight models carry different licences, and some restrict commercial use at scale |
Hugging Face’s own PEFT documentation describes fine tuning a small number of extra parameters instead of the whole model, which keeps training accessible without the cost of a full retrain, and it is worth asking a candidate to explain that trade off in their own words when you hire an LLM engineer in Dubai.
Ways to work with us
How to hire an LLM engineer in Dubai
A dedicated engineer fits a business running an ongoing programme of fine tuning, serving and evaluation work, joining your team and billed monthly. A scoped project fits one clear outcome, such as fine tuning and deploying a single model, delivered with documentation and a handover. Recruitment support fits a business that wants to hire an LLM engineer directly onto its own payroll, with us handling sourcing and the technical assessment. Consulting fits a team deciding whether self hosting is worth it at all, before any infrastructure spend is committed.
Useful before we scope this
- Why a hosted API alone is not enough
- How much labelled or example data you already have
- Expected request volume once live
- Any data residency requirement behind the decision
Assessing a candidate
How to assess an LLM engineer
Checks that separate genuine infrastructure experience from familiarity with a training tutorial.
Ask to see a fine tuning run end to end
The dataset, the method used, and the before and after evaluation scores. A candidate who cannot show the “after” number has not really proven the fine tune worked.
Ask how they decided to self host at all
A considered answer weighs cost, volume and control against the simplicity of a hosted API. An answer that jumps straight to self hosting without that comparison is a warning sign.
Review their evaluation set
Ask where the examples came from and how scoring works. A strong candidate treats this as seriously as the model training itself.
Ask this LLM engineer about a serving problem
Latency under load, memory pressure, or a model that behaved differently once traffic was real. This is common, not an edge case, for anyone who has actually run a model in production.
Check licence awareness directly
Ask which open weight models they have deployed and whether they checked the licence terms before doing so. Not knowing is a bigger risk than it first appears.
Whichever way you hire an LLM engineer in Dubai, dedicated, project, recruitment support or consulting, these checks apply equally, whether you run them yourself or ask us to handle the technical assessment.
Certifications
Certifications and an LLM engineer
Look at a real fine tuning and serving project rather than a certificate for this role.
No single certification covers this work
Fine tuning and serving draw on tools from several projects, including Hugging Face’s libraries and serving engines such as vLLM, referenced above, none of which currently run a certification exam for this kind of engineering work. That leaves anyone who wants to hire an LLM engineer in Dubai judging real projects rather than a badge.
What to look at instead
A fine tuning run with real before and after numbers, a serving setup they can describe under load, and a clear answer on how they chose a licence tell you more than a certificate would, when you hire an LLM engineer in Dubai for infrastructure level work.
UAE considerations
UAE points worth raising with an LLM engineer
Two points that shape a self hosted or fine tuned deployment.
Training data and personal information
If training or evaluation data contains customer or staff information, the UAE’s Federal Decree Law No. 45 of 2021 on personal data protection prohibits processing personal data without consent, with limited exceptions, whatever infrastructure the model runs on. Screen the dataset before training starts, not after.
Where the infrastructure sits
Self hosting or fine tuning at scale usually needs a GPU capable cloud region, and Amazon Web Services operates a region physically in the UAE, named Middle East (UAE), which opened in August 2022. Weigh that against a regional deployment before infrastructure is committed.
This role sits in our AI and machine learning category, part of hire developers in Dubai. If your project is really about building a feature on a hosted model’s API rather than fine tuning or self hosting, our LLM developer page is the simpler starting point. For the wider system around a generative AI feature, retrieval, guardrails and cost control, see our generative AI engineer page, and for production practice around machine learning more broadly, our machine learning engineer page goes further. If the infrastructure question comes first, our cloud services team can help scope where a self hosted model should actually run.
Straight answers
Frequently asked questions
How is an LLM engineer different from an LLM developer?
An LLM developer builds application features on top of a hosted model's API, such as OpenAI's or Anthropic's. An LLM engineer works underneath that layer: fine tuning a model on your own data, serving it on your own infrastructure, and building the evaluation that proves it actually improved.
Do we need to self host a model, or is a hosted API enough?
Most projects are better served by a hosted API, since it removes the infrastructure and maintenance burden entirely. Self hosting earns its cost when you have a specific reason, such as data residency, a very high request volume, or a fine tuned model a hosted provider does not offer.
What does fine tuning actually involve?
In most current practice it means adapting a small number of extra parameters on top of an existing open weight model, using a technique such as LoRA, rather than retraining the whole model from scratch. It needs a clean, representative dataset and a way to measure whether the result is genuinely better, not just different.
Can you evaluate a model we already fine tuned elsewhere?
Yes, this is a common project on its own. We build an evaluation set from real examples of your task, run it against your model and a reasonable baseline, and report where the model actually falls short, before you commit to deploying it further.
Who owns the fine tuned model and the infrastructure?
Ownership of the model weights, the training code and the deployment configuration is agreed in writing in the proposal before work starts. For a self hosted model, you also need to check the base model's own licence before deploying it commercially.
Sources
- Hugging Face: PEFT documentation accessed 14 September 2026
- vLLM: documentation accessed 14 September 2026
- Hugging Face: Evaluate documentation accessed 14 September 2026
Fixed price, in writing
Send your brief. Get a scope and a price within 45 minutes.
- One fixed number, agreed in writing before work starts
- No obligation, and no pressure to sign
- English and Arabic work, with proper right to left layout
- One team for design, marketing, web, media and copy
Got it. Your quote is being written now.
In business hours you will have it within 45 minutes. Check your inbox for the confirmation.