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You will collaborate with various teams, contribute to strategic initiatives, and have opportunities for personal and professional growth. Your responsibilities will include managing the end-to-end lifecycle of AI/ML projects, implementing best practices in MLOps, and mentoring junior team members.\nMissions\nConduire la gestion du cycle de vie des projets d'IA/ML de bout en bout, de la conception à la maintenance.\nMettre en œuvre et promouvoir les meilleures pratiques en matière d'MLOps, y compris la collecte de données, les pipelines d'entraînement des modèles, les déploiements de modèles.\nContribuer de manière significative aux décisions d'architecture des modèles, en tirant parti des techniques d'apprentissage automatique, d'apprentissage profond et d'apprentissage par renforcement.\nProfil recherché\n - 2+ years of experience in building AI/ML models in at least one of the following domains: Ads, relevance, ranking, recommendation systems, and search- PhD with 5+ years of experience or MS with 5-8+ years of industry experience in AI/ML, developing and deploying production-grade ML systems\n- 3+ years of experience in deploying and maintaining ML pipelines in production, including feature engineering and model monitoring frameworks\n- 2+ years of experience in Python and proficiency with distributed frameworks (Spark, Hadoop), SQL, and cloud infrastructure\n- Ability to operate efficiently in a high-paced, multi-functional, and rapidly evolving environment\n- 2+ years of experience with ML packages such as Tensorflow or PyTorch, scikit-learn, and Spark ML\n- 3+ years of experience in building distributed, low-latency, high-throughput batch and online ML services\n- 5+ years of experience in building ML models for ads, search and/or recommender systems including CTR/CVR prediction, ad selection, keyword bidding, and Learning to Rank models\n- 2+ years of experience in building and deploying online experimentation frameworks to identify right models and features at scale\n- 2+ years of experience in building ad selection frameworks using reinforcement learning or contextual bandits\n- Experience in 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