Hire LLM engineers to build generative AI into your product
Named LLM engineers who join your team and build features on large language models: prompts, retrieval, tool calls, evaluation and cost control. You interview them before they start, and you own all the code.
From $45 an hour
AI / LLM engineers at $45/h and senior AI engineers at $60/h. Monthly plans are about 15% lower.
Two profiles in 48 hours
Engineers whose experience matches your model, stack and seniority. You interview them yourself.
Two-week trial
No charge if you stop within the first five working days.
What does an LLM engineer build?
An LLM engineer builds software features on large language models. Typical work includes assistants inside a product, document extraction and summaries, search that answers questions, and agents that take actions. The job is mostly engineering around the model: data, prompts, tool calls, testing, cost and monitoring, so the feature behaves well for real users.
Generative AI developer and LLM engineer are often two titles for the same job. Both build products on models that generate text, code or structured data. What matters is whether the person can ship that feature and keep it reliable.
Assistants inside your product
A help panel, a writing aid or a setup assistant that knows your product and the user's context.
Extraction and classification
Reading invoices, emails, forms or tickets, then turning them into structured fields your systems can use.
Search that answers questions
Answers drawn from your own documents, with sources. This is retrieval-augmented generation; see RAG development.
Agents that take actions
Models that call your APIs to look things up, update records or pass a case to a person. See AI agent developers.
Summaries and drafts
Meeting notes, case summaries, report drafts and replies that a person checks before they go out.
Internal tools
Small LLM features that save your own team time, such as tagging, triage and first drafts.
Most of these features need the same core work. Someone has to choose the model, shape the input, test the output on real examples and watch the cost. That is the LLM engineer's job.
What skills should a senior LLM engineer have?
A senior LLM engineer should write production Python or TypeScript and design prompts with structured outputs. They build retrieval and tool calling, and measure quality with evaluation sets. They also control token cost and latency, add guardrails against misuse, and explain model trade-offs in plain language.
- Production code. Clean Python or TypeScript, with tests, code review and version control, not only notebooks.
- Prompt and output design. Clear instructions, good examples, and structured output such as JSON that the rest of your code can check.
- Retrieval. Splitting documents, creating embeddings, searching a vector index and passing the right context to the model.
- Tool calling. Letting the model call your functions and APIs, with ordinary code checking every argument.
- Evaluation. Building a test set of real inputs, scoring outputs, and catching regressions when a prompt or model changes.
- Cost and latency. Counting tokens, caching, streaming, and choosing a smaller model where it is good enough.
- Guardrails. Handling prompt injection, filtering unsafe output and keeping personal data out of logs.
- Model choice. Knowing when a hosted API, an open-weight model or fine-tuning is the right choice, and why.
- Observability. Logging prompts, responses and failures so problems can be traced and fixed.
- Communication. Explaining limits and risks to product owners without hype, and writing decisions down.
Mid-level engineers can do much of this with guidance. A senior engineer decides how the pieces fit together and owns the result. Our senior AI engineer tier is for that role, at $60 an hour.
Our rates
Published prices in US dollars. Monthly plans are 160 hours, about 15% below the hourly rate.
Mid-level developer
3 to 5 years
$25 / hour
$3,400 a month (160 h)
Senior developer
6 or more years
$35 / hour
$4,750 a month (160 h)
AI / LLM engineer
Agents, RAG, IBM watsonx
$45 / hour
$6,100 a month (160 h)
Senior AI engineer
Leads AI architecture and delivery
$60 / hour
$8,150 a month (160 h)
Two-week trial with the developer you choose. No charge if you stop within the first five working days. US Eastern clients get 09:00 to 12:00 US Eastern time through an evening shift, at 15% extra. Full pricing and comparisons.
Can you hire OpenAI, ChatGPT and Claude API developers?
Yes. Our LLM engineers work with the model provider you have chosen, whether that is OpenAI, Anthropic's Claude API, an open-weight model or IBM watsonx. Most skills carry across providers: prompts, structured output, tool calls, streaming and evaluation. Tell us your provider and we put forward engineers whose experience matches it.
"Hire ChatGPT developers" usually means hiring someone to build on OpenAI's models through the API. ChatGPT itself is OpenAI's app for end users. Your product talks to the model through the API, under your own account, keys and usage limits.
Swipe the table sideways to see every column.
| Option | What it means | Teams often choose it when | Things to weigh |
|---|---|---|---|
| Hosted model API, such as OpenAI or Anthropic | You send requests to the provider's model over the internet and pay per token. | You want strong models quickly, without running servers. | Per-token cost at scale, the provider's data terms, and model versions that change over time. |
| Open-weight model | You download model weights, often from Hugging Face, and run them on servers you control. | Data must stay inside your network, or usage is high enough that hosting costs less. | You run the GPUs, the updates and the scaling yourself. |
| IBM watsonx | IBM's platform for building and running AI models, including governance tools. | Your company already works with IBM, or wants its AI governance features. | How well the platform fits the rest of your stack. |
| Offline or on-premise LLM | An open-weight model installed on your own servers or private cloud, with no calls to outside services. | Regulation or policy rules out sending data to third-party AI APIs. | Hardware cost, and the effort to keep the model current. |
Webify.AI is an IBM Partner and deploys on IBM watsonx and open-weight models. For OpenAI, Anthropic and other providers, we work with the account you already have.
Switching provider later is easier if the code keeps the model behind one small interface. A good LLM engineer builds it that way from the start. You can then compare models on your own test set.
When do you need Hugging Face developers and open-weight models?
Open-weight models suit teams whose data cannot leave their network, or whose usage is high enough that self-hosting costs less. Hugging Face is the main public hub for these models. Developers who know it can choose a model, test it on your data and run it on your own infrastructure.
Open-weight models are models whose weights are published, so you can run them yourself. The terms of use differ from model to model. Some allow commercial use freely and some add conditions, so check them before you build on one.
- Choosing a model. Comparing candidates on your own examples, not only on public leaderboards.
- Serving. Running the model behind an API on your GPUs, with batching and sensible limits.
- Making the model smaller. Reducing its numeric precision so it fits cheaper hardware, then checking how much quality it loses.
- Fine-tuning. Training the model further on your examples when prompts and retrieval are not enough.
- Updates. Moving to newer models as they are released, with tests that show whether quality went up or down.
Webify.AI also builds offline and on-premise LLMs. These are open-weight models running on your own servers or private cloud, so prompts and documents stay inside your network. See offline LLM development if that is what you need.
What is LLM fine-tuning, and when is it worth doing?
LLM fine-tuning means training an existing model further on your own examples, so it learns a format, a style or a narrow task. It is worth doing when good prompts and retrieval still fall short, and you have enough high-quality examples. It does not reliably teach a model new facts; retrieval is better for that.
Swipe the table sideways to see every column.
| Approach | What it changes | Best for | Effort |
|---|---|---|---|
| Prompting | The instructions and examples sent with each request. | Most features. Always the first thing to try. | Low. You change text and re-run your tests. |
| Retrieval (RAG) | The context the model sees, pulled from your documents at question time. | Answers that depend on your own, changing information. | Medium. You build and maintain a search index. |
| Fine-tuning | The model's weights, through extra training on your examples. | A consistent format or tone, a narrow classification task, or a smaller model doing a larger model's job. | Higher. You need clean examples, training runs, and re-testing for each new base model. |
The approaches combine. Many production systems use retrieval and careful prompts, and fine-tune only one step.
A common method is LoRA (low-rank adaptation). It trains a small set of extra weights instead of the whole model, which cuts the hardware needed. Some hosted providers also offer fine-tuning through their APIs for some of their models.
Before fine-tuning, an LLM engineer should show you a baseline. That means scores for prompting and retrieval on the same test set. If fine-tuning cannot clearly beat that baseline, it is not worth the upkeep.
Should you hire a prompt engineer or an LLM engineer?
For most products, hire an LLM engineer. Prompt engineering is one part of the job, but a production feature also needs code, data handling, testing and monitoring. A prompt specialist suits a narrow task, such as improving prompts in a working system. Our engineers do prompt work as part of building the whole feature.
If you came here to hire prompt engineers, it helps to know what good prompt work looks like in production. It is closer to software testing than to clever wording.
- Prompts live in your repository, under version control, not in someone's notes.
- Every prompt change runs against a test set before it ships.
- Outputs follow a schema, so your code can check them and reject bad ones.
- Examples in the prompt come from real cases, with personal data removed.
- Text from users and documents is treated as untrusted, to limit prompt injection.
We don't offer a separate prompt-engineer role. Prompt work is billed at the AI / LLM engineer rate of $45 an hour, like the rest of the build.
How do you interview an LLM engineer?
Ask them to walk through an LLM feature they built: the model choice, the testing, what failed and the running cost. Then give them a short task on your own data. Good candidates talk about evaluation and failure cases unprompted, and say plainly what a model cannot do.
You interview every engineer we put forward before they start. We sign your NDA before the first call. That way you can talk about your real product.
- "Show me a prompt you wrote and the test set behind it." Listen for real examples and a way of scoring them, not "it looked good".
- "How did you get reliable structured output from a model?" Listen for schemas, validation and a plan for bad responses.
- "How did you decide how much context to send?" Listen for measured trade-offs between quality, speed and token cost.
- "How would you stop a user from making the model ignore its instructions?" Listen for several layers of checks, not one filter.
- "When would you not use an LLM?" A good engineer has a clear answer.
The two-week trial then shows how they work on your real backlog. There is no charge if you stop within the first five working days.
What does an LLM engineer cost, and what else will you pay for?
An AI / LLM engineer from Webify.AI costs $45 an hour, or $6,100 a month for 160 hours. A senior AI engineer costs $60 an hour or $8,150 a month. UK clients pay £36 and £48 an hour. Model usage and hosting are billed separately by your own providers.
- Model usage. Hosted APIs charge per token, for input and output. Your engineer can estimate this from expected traffic before you build.
- Hosting. Open-weight models need GPU servers, owned or rented. A smaller model often does the job for less.
- Retrieval infrastructure. A vector index, in PostgreSQL with pgvector or a separate service, if the feature answers from documents.
- Evaluation time. Building and maintaining the test set takes engineering hours. It is the cheapest way to avoid shipping a broken feature.
- Upkeep. Models and provider APIs change. Budget some hours each month to re-test and adjust.
For comparison, KORE1's 2026 figures for US staffing-agency bill rates put AI / ML engineers at $140 to $240 an hour (KORE1). In the UK, our AI / LLM engineer works out at £288 for an eight-hour day. Every rate is on our pricing page.
How does hiring an LLM engineer through Webify.AI work?
Tell us your model, stack and the feature you want built. Within 48 hours we send two named profiles whose experience matches. You interview them, choose one, and start with a two-week trial. After that you continue hourly from 80 hours a month, or move to a monthly plan of 160 hours.
- 1
Tell us what you are building
The model or provider, your languages and frameworks, the seniority you need and when you want to start.
- 2
Get two profiles in 48 hours
Named engineers whose experience matches your stack, and who are free on your start date.
- 3
Interview them yourself
We sign your NDA before the first call. You choose who joins your team, or ask for other profiles.
- 4
Start the two-week trial
Two-week trial with the developer you choose. No charge if you stop within the first five working days.
- 5
Carry on hourly or monthly
Hourly plans start at 80 hours a month and are billed every two weeks against timesheets. Monthly plans (160 hours) are billed in advance. US clients pay in USD, UK clients in GBP.
Webify.AI is headquartered in Ahmedabad, India, and our engineers work from there. Overlap for UK teams: 09:00 to 13:00 UK time every working day, inside a normal Indian working day, at no extra cost. Overlap for US teams: 09:00 to 12:00 US Eastern time through an evening shift, at 15% extra. There is more detail for UK companies and US companies.
How are your code and data protected?
We sign your NDA before the first call, and the IP in all code is assigned to you. Engineers are named in the contract, and nothing is subcontracted without your consent. A person reviews all AI-assisted code, and your code never goes into public AI tools. Our full approach is on the security page.
- We sign your NDA before the first call.
- You own all the code: IP is assigned to you.
- Developers are named in the contract and never swapped without your agreement.
- No subcontracting without your consent.
- A person reviews all AI-assisted code, and your code never goes into public AI tools.
LLM work adds one more question: where prompts and data go. Your engineer uses the model accounts you control, so requests, logs and keys stay under your terms. Read how we handle security for the rest.
Frequently asked questions
Can an LLM engineer take over an AI feature we already have?
Yes. A sensible first task is to add a test set to the existing feature and measure it. You then know how well it works today, and every later change can be checked against that baseline.
Which programming languages do your LLM engineers use?
Mainly Python, which most model SDKs and AI libraries support first. For features inside a web product they also work in TypeScript and Node.js, alongside React or Next.js front ends.
Will a model provider train on our data?
That depends on the provider's terms and your account settings, not on us. Check them before you send real data. Your engineer can set the feature up under your own account, or suggest an open-weight model if data must stay in-house.
How do you stop an LLM from making things up?
You can't remove it entirely, but you can make it rare and visible. Ground answers in retrieved sources, ask for citations, let the model say it doesn't know, and test for invented details on every release.
Can the engineer help us choose between models?
Yes. They build a test set from your real inputs, run the candidate models against it, and compare quality, speed and cost. You then choose with numbers in front of you, not from benchmarks made for other tasks.
Can an LLM engineer reduce our model costs?
Often, yes. Common savings come from shorter prompts, caching repeated requests, sending less context, and using a smaller model for simple steps. The engineer measures cost per request first, so each change can be checked.
Who owns the prompts and evaluation sets?
You do. Prompts, test sets and configuration live in your repositories with the rest of the code, and the IP is assigned to you. If the engagement ends, all of it stays with you.
Do LLM engineers need access to our production data?
Not at first. They can start with sample or de-identified data, and with test sets built from real but cleaned examples. Production access can be added later, through your own accounts and permissions.
Can one engineer build both the LLM feature and the interface?
For a small feature, often yes. For larger work, pair an AI / LLM engineer with a mid-level developer at $25 an hour. The developer builds the interface and integrations, and the LLM engineer focuses on the model side.
Can LLM features work in several languages?
Yes. Most large models handle many languages, but quality varies by language and task. The engineer adds test cases in each language you support, so gaps show up before your users find them.
Ask for two LLM engineer profiles
Tell us the role and when you need someone. We'll email two named engineers with their experience and availability within 48 hours, and you choose who to interview.
- No cost to receive profiles
- Two-week trial with the developer you choose. No charge if you stop within the first five working days.
- Invoiced in USD or GBP; IP assigned to you.