Senior ML Ops _TG Content Creation
2 months ago
Le Content Creation Technology Group (CCTG) est à la recherche d'un ML Ops senior qui travaillera au sein d'une équipe qui développe des solutions d’apprentissage automatique pour améliorer l'expérience joueur, entre autres avec un projet sur des robots visant à simuler de vrais joueurs concurrents. Le rôle du ML Ops senior sera de définir les meilleures pratiques et les principes de conception pour aider l'équipe à livrer notre ambitieuse feuille de route de fonctionnalités.
Principales responsabilités :
- Concevoir et développer des systèmes d'apprentissage automatique et des schémas basés sur les requis et les objectifs de l'entreprise.
- Optimiser les solutions existantes pour améliorer les performances, l'évolutivité et l'efficacité.
- Adapter et améliorer l'utilisation des modèles, faciliter l'utilisation des outils et les adapter aux besoins des utilisateurs.
- Créer des outils pour soutenir et intégrer les modèles d'apprentissage automatique dans un pipeline multi-processus.
- Améliorer les librairies et frameworks d'apprentissage automatique existants.
- Collaborer avec les équipes de production du jeu, l'équipe de science des données, l'équipe de recherche et d'autres parties prenantes.
Autres responsabilités :
- Mener des recherches pour rester au courant des dernières avancées technologiques.
- Documentation, présentations et partage des connaissances pour communiquer des concepts d'IA complexes aux collaborateurs techniques et non techniques.
- Création de preuves de concepts pour satisfaire les exigences sur les orientations du produit.
Qualifications
Formation :
- Baccalauréat en informatique ou en génie informatique ou équivalent.
- Une spécialisation en apprentissage automatique est un atout.
Expérience pertinente :
- Un minimum de 5 ans d'expérience en génie logiciel.
- Un minimum de 2 ans d'expérience en apprentissage automatique.
Compétences :
- Une bonne connaissance d'un pipeline d'apprentissage automatique de production ; de la formation et du déploiement de ML au suivi de la performance des modèles et au behavioral-drift.
- Maîtrise de Python et expérience des librairies et frameworks tels que PyTorch et/ou TensorFlow.
- Une bonne connaissance des plateformes MLOps telles que Databricks et/ou ClearML.
- Une bonne connaissance de la technologie des conteneurs tels que Docker et/ou Kubernetes.
- Une bonne connaissance des fondamentaux de l'apprentissage automatique et de l'apprentissage profond.
Intéressant :
- Une bonne connaissance des plateformes basées sur le cloud est un atout.
- Une bonne connaissance des produits de base de données est un atout.
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