Hire AI DevelopersVetted by Miracuves · Inside Your Team or as a Dedicated AI Pod

What does a dedicated AI developer build, and what stays with your team?

A Miracuves AI developer builds the parts of your product that depend on a model: the prompts and structured output of an LLM feature, the retrieval pipeline behind a RAG assistant, the tools an AI agent may call, a classifier or forecast trained on your records, or a computer vision step that reads images and video. They also build what keeps those parts honest in production: evaluation sets, request logging, cost tracking and a fallback when the model is unsure.

What stays with you is product ownership and people management. Your product lead decides which use case matters and what counts as good enough; your managers run one-to-ones and reviews. The developer works inside your repositories, tickets and release process, not in a side project.

LLM application engineer, ML engineer or MLOps engineer: which do you need?

Start from the problem, not the job title. If the feature reads, writes or reasons over text, such as a support assistant, document search or an agent that files tickets, you need an LLM application engineer. If you are predicting a number or a label from your own records, such as churn, fraud, demand or a product category, hire machine learning engineers. If models already exist but deployments are manual, nobody notices when quality drops and the GPU bill keeps surprising you, you need an MLOps engineer; MLOps services cover that work as a project.

Many products need two of these at different times. A common path is an LLM application engineer first, with MLOps capacity added once the feature carries real traffic. Moving between roles changes the engagement letter, not the contract.

Is retrieval and prompting enough, or do you need to fine-tune a model?

For most business use cases, retrieval comes first. A RAG pipeline hands a general model the right passages of your documents at question time, so answers stay current when the documents change and every answer can point to its source. Fine-tuning changes the model itself; it helps when you need a fixed output style, a narrow classification task at high volume, or a smaller model that is cheaper to run.

A good AI developer proves the need before training anything: build the retrieval version, score it against an evaluation set, and fine-tune only if the numbers show a gap retrieval cannot close. For a retrieval build scoped as a project rather than a hire, see RAG development.

What should you have ready before an AI developer starts?

The first weeks go faster when four things are settled before day one. None of them has to be perfect, but each one missing is time the developer spends waiting instead of building.

• One use case with a success measure, for example the share of support questions answered correctly without a person
• Access to the data the feature needs, and a named person who can approve what may be sent to an external model
• A few dozen real examples with the answer you would accept, which become the first evaluation set
• Model API accounts or a cloud budget in your company's name, so usage, keys and bills stay yours

What does an AI feature cost to run, beyond the developer's rate?

The monthly rate covers the engineer. Running the feature has its own costs, billed by your providers to your accounts: model API usage, which grows with traffic and with the length of each prompt; embeddings and vector database hosting for retrieval; GPU instances if you self-host an open-weight model; and logging and monitoring.

Treat cost as a design input, not an invoice surprise. Expect your developer to report cost per request next to answer quality, cache repeated calls, send simple requests to a smaller model and cap how much context each request carries. This page quotes no model prices because providers change them often; your developer estimates them from your own traffic in the first weeks.

How do you judge an AI developer's first month?

By numbers you can rerun, not by a good demo. A demo shows the questions a feature handles well; an evaluation set shows how often it fails. By the end of the first month you should be able to open all of these in your own systems.

If a month passes without them, raise it with your Miracuves contact. Replacement is free in the first two weeks, and after that the notice period is two weeks.

• An evaluation set built from your real examples, with a baseline score
• A working version behind a feature flag or in a staging environment
• A log of failure cases grouped by cause: retrieval missed, model misread, data missing
• Cost per request and response time for the current design
• A short written plan for the next month, ranked by expected gain