Community 01 · Artificial intelligence
Fair4IA, artificial intelligence freelancers validated by their peers.
Independent AI specialists who have put models into production, not only into a notebook. Chosen by their peers, supported by one of them, with a 10% margin written on the quote.
The community’s profiles.
The word “AI” covers roles that share neither tools nor deliverables. A RAG architect and a computer vision expert do not do the same job, and a company looking for a freelance data scientist does not necessarily need an MLOps engineer. The community is therefore organized into four families.
Designing and training models
Machine learning engineer
Designing, training and industrializing learning models.
Data scientist
Exploring data, forming hypotheses and building models to answer a business question.
NLP expert
Natural language processing, classification and extraction from text.
Computer vision expert
Computer vision models, image and video analysis.
Fine-tuning specialist
Adapting existing models to a given domain or task.
Building generative AI applications
Generative AI and LLM engineer
Applications built on language models, from design to evaluation.
RAG architect
The whole document chain: chunking, embeddings, vector database, reranking, evaluation.
Prompt / context engineer
Designing the instructions and context given to models, and measuring their effects.
Agentic AI engineer
Agents able to call tools and act within the information system.
Deploying and operating in production
MLOps engineer
Deployment, experiment tracking, model registry, drift monitoring.
Data engineer for AI
Data flows and datasets prepared for training and running models.
AI architect
Architecture and platform choices for AI systems in production.
Scoping, steering, ensuring compliance
AI product manager
Scoping the need and steering an AI-based product.
AI project manager
Running a project across data teams, business teams and management.
AI ethics and compliance lead
Risk classification, technical documentation and compliance with the European regulation.
AI strategist
Roadmap and prioritization of AI uses across the company.
Data flows, platforms and data governance belong to Fair4Data; Fair4IA takes over for models, assistants and getting them into production. When a need touches both, the two community leads talk to each other.
The tools our experts work with.
A freelance LLM expert joining a team has to be productive on the tooling already in place, not impose their own. The technologies most often requested on the community’s missions:
- Python
- PyTorch
- TensorFlow
- LangChain
- LlamaIndex
- Hugging Face
- Vector databases
- MLflow
- Databricks
- Azure ML
- Vertex AI
- SageMaker
Five situations where companies turn to Fair4IA.
01
An internal assistant that answers from your own documents.
The most common need, and the one where the gap between demo and production is widest. A RAG consultant works across the whole chain: document chunking, choice of embedding model, vector database, result reranking, answer quality evaluation and safeguards against invented answers.
02
Industrializing a POC that never leaves the lab.
A convincing prototype does not become a service just by going online. Data ingestion, training reproducibility, latency, inference cost, drift monitoring and rollback in case of incident all have to be reworked. This is the territory of a freelance machine learning engineer backed by an MLOps engineer.
03
A compliance audit ahead of the AI Act deadline.
European regulation requires systems to be classified by risk level, datasets to be documented and decisions to be traced. An AI Act compliance specialist maps the uses already in place in the company, identifies those that move into high risk and writes the expected technical documentation.
04
Migrating or taking over an MLOps platform.
Moving from an in-house scheduler to a managed platform, taking over an abandoned model registry, setting up experiment tracking: a freelance MLOps engineer scopes the target, migrates what exists without interrupting the models in service and hands operations over to the internal teams.
05
Business agents connected to the information system.
An agent that reads a mailbox, queries an ERP and drafts a reply raises architecture questions before model questions: access rights, logging, error recovery, cost per run. A generative AI consulting mission for a company almost always starts with this scoping.
Joining Fair4IA.
You enter Fair4IA through a 30-minute meeting with its community lead, not through open registration: a directory accepts everyone and leaves the client to sort, Fair4IA does the opposite. Across all communities, one profile in three presented is retained.
- 1A 30-minute video call with the community lead. The discussion covers work actually delivered: which data, which constraints, which trade-offs, what failed and why. A personal project or a certification does not replace a production deployment.
- 2The community lead validates your entry. If your background fits, they tell you during the call and ask whether you prefer a profile pre-filled from your LinkedIn page, or a CV you send. They then validate your entry: criteria reviewed with the ecosystem’s experts, references checked with previous clients.
- 3You receive an invitation to the talent space. An invitation link lets you create your password and enter. If the link has not been opened, a reminder goes out three days later.
The same price as everywhere at Fair4B.
Fair4B applies a 10% margin, stated to the client from the brief, before any candidate is presented. An equivalent share, 10%, is redistributed to ecosystem members across five roles: business referral, selection, negotiation, mission follow-up and sponsorship.
On an AI mission, this means two concrete things. The client knows the share that goes to the expert and the share that goes to Fair4B, so they can compare one offer with another on a real basis. And the community lead who ran the interview of the selected consultant receives a share of this redistribution: the quality of selection is rewarded, not just claimed.
Questions about Fair4IA.
How much does an AI freelancer cost in France?
An AI freelancer’s daily rate is built on three variables: how rare the specialty is, the number of years spent running models in production, and the level of responsibility on the mission. Fair4B states its margin, 10%, from the brief: the client therefore knows exactly what share goes to the expert.
What is the difference between a data scientist and a machine learning engineer?
A data scientist formulates hypotheses, explores data and builds models to answer a business question. A machine learning engineer industrializes those models: training pipelines, production deployment, drift monitoring, inference cost. The first looks for the answer, the second makes it reliable and reproducible.
How does Fair4B select its AI experts?
In three steps. A 30-minute meeting with the Fair4IA community lead, on work actually delivered. The lead then validates the entry, drawing on the ecosystem’s experts and having references checked with previous clients. Then the invitation to the talent space. This selection is one of the five roles paid through redistribution.
Does Fair4B offer generative AI and LLM experts?
Yes. The Fair4IA community brings together generative AI and LLM engineers, RAG architects, fine-tuning specialists and prompt engineers. Missions range from the internal document assistant to the business agent connected to the information system, including model evaluation.
How quickly does Fair4B propose a shortlist of AI experts?
Fair4B replies within 24h and presents its first profiles within 48h of receiving the brief. These timeframes come from the circuit: the request goes straight to the Fair4IA community, whose members know their peers’ availability.
What is the difference between Fair4IA and an AI-specialized IT services firm?
An IT services firm charges a margin it does not disclose. Fair4B states 10% from the brief and redistributes an equivalent share to the members who made the mission possible. Selection is led by the Fair4IA community lead, who draws on the ecosystem’s experts, not by a sales team.