Recruter un talent
Recrutez un Machine Learning Engineer
Specialized engineers who build, train, and optimize machine learning models for production environments. They focus on model performance, scalability, and reliability in real-world applications.
D\u00e8s $12-18/hrMise en relation sous 48hProfessionnels v\u00e9rifi\u00e9s
Comp\u00e9tences cl\u00e9s
Chaque Machine Learning Engineer sur la plateforme AI10 est v\u00e9rifi\u00e9 pour ces comp\u00e9tences essentielles.
ML Frameworks (Scikit-learn, XGBoost)
Feature Engineering & Selection
Model Optimization & Tuning
Data Pipeline Development
Tarification
Tarification transparente et abordable pour les meilleurs talents IA.
\u00c0 partir de
$12-18/hr
- Professionnels v\u00e9rifi\u00e9s et test\u00e9s
- Horaire flexible ou par projet
- Mise en relation sous 48 heures
- Gestion de projet d\u00e9di\u00e9e
- Garantie de remplacement
FAQ
Questions fréquemment posées
What's the difference between an ML Engineer and an AI Engineer?
ML Engineers focus specifically on the model development lifecycle — feature engineering, algorithm selection, hyperparameter tuning, and model validation. AI Engineers have a broader scope including system architecture, deployment infrastructure, and integration with business applications.
What types of ML models can your engineers build?
Our ML Engineers build classification, regression, clustering, recommendation, time-series forecasting, and anomaly detection models. They work with both traditional ML (XGBoost, Random Forest) and deep learning (transformers, CNNs, RNNs) depending on the problem.
How do you ensure ML model quality?
Every model goes through rigorous validation: cross-validation, holdout testing, and domain-specific evaluation metrics. We also implement model monitoring in production to detect data drift, performance degradation, and bias — with automated retraining triggers.
Can ML Engineers work with our existing data infrastructure?
Yes. Our ML Engineers integrate with your existing data warehouses (Snowflake, BigQuery, Redshift), feature stores, and MLOps platforms. They adapt to your tech stack rather than forcing a new one.