Profiles in ~48 hoursVetted shortlist after your brief
Start within a weekAfter a short interview or trial
You own the codeIP assignment and NDA
Scale with 30 days noticeAdd or release engineers
Trusted by founders and enterprises worldwide
About hiring LLM engineers
Everything to know before you hire LLM talent
Hire LLM developers when a language model is central to your product and you need people who understand more than the API call. Speqto engineers handle model selection, prompting, retrieval, fine-tuning, self-hosted serving and evaluation, for teams that want measurable quality and sensible running costs.
The first decision is rarely technical glamour. It is whether you should prompt a hosted model, add retrieval, fine-tune a smaller model, or host something open-weight yourself. An experienced LLM developer will price each route against your volume, privacy rules and latency target before writing a line of production code. Cost matters as much as quality here: a model that is slightly better but ten times dearer per request can sink a feature that looked excellent in a demo.
Most wins come from structure around the model: clear system prompts, JSON schemas for outputs, retries with validation, and context built from the right records. Fine-tuning helps with style, format and narrow tasks, but it rarely fixes missing knowledge, which is a retrieval problem. Good developers also know the limits of long context windows, since stuffing a prompt with documents often lowers accuracy, raises latency and hides which source supported an answer.
What we check in an LLM developer
We ask candidates to walk through a real deployment: how they chose the model, how they measured answer quality, what broke in the first week and what they changed. We look for fluency in tokenization limits, context-window trade-offs, embeddings, vector indexes, quantisation and batching. Open-source contributions, written evaluations or a walkthrough of a deployed system help us confirm the depth behind a profile, because buzzwords on a CV are easy to produce.
On the engineering side we want typed code, tests around non-deterministic outputs, tracing for every call and a habit of versioning prompts like code. Familiarity with parameter-efficient methods such as LoRA is useful for fine-tuning work, and so is knowing when not to use them. We deliberately include a question about a time the model was confidently wrong and how they found out, since the answer reveals how seriously they treat measurement.
Working with LLM developers at Speqto
Share your use case and constraints, such as data residency or a target latency, and we send suitable profiles within about two business days. Engineers join your tools, attend your standups and demo working builds every week. That sprint produces a short written recommendation covering model choice, expected cost per thousand requests, privacy considerations and the evaluation set you can reuse as the project grows.
You can start with one developer on a discovery sprint to test feasibility against your own data, then extend. Teams scale with 30 days notice. We sign NDAs on request, assign all IP to you, and pair LLM developers with backend, front-end and QA engineers when the feature needs a full product around it. Feature work alongside the model, such as rate limiting, queues, caching and user-facing error states, is where most delivery time goes, so we staff for that as well.
Why teams hire from Speqto
A bench, not a single freelancer
If an engineer is unavailable or the fit is wrong, a replacement comes from the same bench, with knowledge handed over.
Side-by-side tests of hosted and open-weight models on your own tasks, scored for accuracy, latency and cost per request.
Retrieval-augmented generation
Document parsing, chunking, embeddings and hybrid search so the model answers from approved sources and can show where each answer came from.
Fine-tuning and adaptation
LoRA and instruction tuning for style, format or narrow domain tasks, with before and after evaluation to prove the gain.
Self-hosted model serving
Deploying open-weight models with vLLM or similar on your cloud, with batching, quantisation and autoscaling for predictable throughput.
Prompt and output engineering
Versioned prompts, structured outputs, validation and retry logic so downstream systems receive data they can trust. Prompts live in version control with named owners.
Evaluation and monitoring
Regression suites, LLM-as-judge checks calibrated against human review, and production tracing to catch drift early. Alerts fire when quality or cost moves unexpectedly.
Skill set
Skills and tools your LLM hire brings
Matched against your stack during screening, not listed for decoration.
Python
PyTorch
HUHugging Face Transformers
PEPEFT / LoRA
VLvLLM
Ollama
LangChain
LlamaIndex
pgvector
QDQdrant
FastAPI
WEWeights & Biases
How we vet
Five filters before you see a profile
You only interview engineers who have already cleared code review, a live problem-solving session and a communication check.
1Role brief matched to our bench
2Code and project review
3Technical assessment
4Live problem solving
5Communication and culture check
How to hire
Hire LLM talent in four steps
A short, low-risk path from your brief to a working engineer. If the first engineer is not right, we replace them.
1
Share your brief
Tell us the role, stack, hours overlap and how long you need the engineer. A 20-minute call is usually enough.
2
Get vetted profiles
In about two business days you receive candidates with assessment notes, code samples and availability.
3
Interview and trial
Speak to the engineers yourself. Start with a short paid trial and continue only if the fit is right.
4
Onboard and ship
Your engineer joins stand-ups and repositories, demos weekly and is backed by a delivery lead.
Included
Every engineer comes with
No hidden extras: the basics of a safe, managed engagement are part of the package.
NDA and IP assignment
Signed before details are shared; everything built is yours.
Weekly progress reports
Plain-language updates and a demo of working software.
Replacement from our bench
If the fit is wrong, a new engineer joins with a proper handover.
Your tools and repositories
Your Git, tickets and chat; we adapt to your process.
Equipment and payroll handled
Hardware, leave and compliance stay with Speqto.
30 days notice to scale
Add engineers or release them as your roadmap changes.
Compare options
Speqto, freelancer or in-house hire
Speqto dedicated engineer
Freelancer
In-house hire
Time to start
About one week
Days, but quality varies
Often two to four months
Vetting
Practical assessment and senior review
You test every candidate
You run the whole process
If someone leaves
Replacement from our bench
You start the search again
Gap until the next hire
Management
Delivery lead plus your direction
Entirely yours
Entirely yours
Scaling
Add or release with 30 days notice
One person at a time
Slow and costly to reverse
IP and NDA
Assigned to you by contract
Depends on the agreement
Standard employment terms
Equipment and payroll
Handled by Speqto
Their own
Yours
FAQ
Hire LLM Developers FAQs
Straight answers on cost, timelines, IP ownership, team setup and how we work. Cannot find yours? Ask a solutions architect directly.
Use retrieval when the problem is missing or changing knowledge, such as policies or product data. Fine-tune when you need consistent tone, format or a narrow skill. Many production systems combine a prompted base model, retrieval and a small tuned model for one step.
Can you host an LLM inside our own cloud for privacy?
Yes. Our developers deploy open-weight models on your cloud account or on-premise GPUs and keep data inside your boundary. The trade-off is operational effort and usually lower raw capability than the largest hosted models. Expect more operational work than with a hosted API.
How do you measure the quality of LLM output?
With a test set built from real user questions, scored by exact checks where possible and by calibrated model judges plus human spot checks where not. The same set runs on every prompt or model change.
How quickly can an LLM developer start?
Profiles matching your brief reach you in about two business days. Start dates depend on interviews and your onboarding, and we sign an NDA first if you ask for one. If your requirements narrow the pool, we say so early and suggest alternatives.
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