You will get a custom LLM fine-tuning from your data to a production-ready model
Rising Talent

Rising Talent

Project details
9+ years shipping production ML systems. I fine-tune LLMs on your data end to end — from data audit to a production-ready model with proper evaluation. You get full experiment tracking, an error analysis report, and model weights in your preferred format.
AI Algorithms
Large Language ModelAI Applications
AI Chatbot, AI Content Creation, AI Text-to-Speech, AI-Generated Code, Conversational AI, Machine Translation, Natural Language Generation, Synthetic Data Generation, Text RecognitionAI Development Language
PythonAI Models
ChatGPT, LLaMAWhat's included
| Service Tiers |
Starter
$5,500
|
Standard
$11,000
|
Advanced
$16,500
|
|---|---|---|---|
| Delivery Time | 14 days | 21 days | 28 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | - | - | - |
Batch Normalization | - | - | - |
Database Integration | - | - | - |
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | - |
Model Documentation | |||
Model Monitoring | - | - | - |
Model Testing & Optimization | - | - | - |
Model Tuning | |||
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | |||
Source Code |
Frequently asked questions
About Maxence
Senior AI/ML Engineer | LLM, RAG, Agents
Paris, France - 12:22 pm local time
9+ years turning complex ML research into shipped products. Real systems running in production.
Steps for completing your project
After purchasing the project, send requirements so Maxence can start the project.
Delivery time starts when Maxence receives requirements from you.
Maxence works on your project following the steps below.
Revisions may occur after the delivery date.
Data audit & training plan
I review your dataset quality, select the base model, define eval metrics, and deliver a training plan.
Fine-tuning & iteration
I train the model, iterate on hyperparameters, and compare runs with full experiment tracking (W&B/MLflow).

