AI Engineer
il y a 3 jours
Ingénieur IA | Python-RAG
? Développement d?algorithmes : concevoir et développer des algorithmes d?apprentissage automatique (machine learning), d?apprentissage profond (deep learning) et d?autres techniques d?IA.
? Analyse de données : collecter, nettoyer et analyser les données pour entraîner les modèles d?IA.
? Optimisation des modèles : améliorer la précision et l?efficacité des modèles d?IA existants.
? Intégration de solutions IA : intégrer les solutions d?IA dans les systèmes existants ou développer de nouvelles applications basées sur l?IA.
? Recherche et innovation : mener des recherches pour explorer de nouvelles techniques et technologies en IA.
? Collaboration interdisciplinaire : travailler avec des équipes multidisciplinaires, y compris des experts en données, des développeurs de logiciels et des chercheurs.
? Veille technologique : suivre les avancées et les tendances dans le domaine de l?IA pour anticiper les évolutions et proposer des améliorations.
? Documentation et reporting : rédiger des documents techniques, des rapports d?avancement et des publications scientifiques pour partager les résultats et les connaissances acquises.
Profil Recherché
BAC+4/+5
3 ? 5 ans d?expérience en IA, Machine Learning ou Data Science
Très bon niveau en Python & RAG, SQL, NoSQL
Profil candidat:
Diplôme en informatique, data science, intelligence artificielle ou domaine similaire (Bac+5 recommandé, ou Bac+3 + avec solide expérience).
Bonne maîtrise de Python et des principaux frameworks ML / IA (ex. TensorFlow, PyTorch, Keras).
Expérience avec le traitement des données (Big Data, pipelines, bases de données, nettoyage, transformation)
Connaissances en déploiement (API, microservices), éventuellement en cloud (AWS / GCP / Azure), conteneurs / orchestration (Docker, Kubernetes)
Capacités de collaboration et communication : travailler avec des data scientists, des développeurs, des équipes métiers pour aligner les solutions IA sur les besoins.
Rigueur, autonomie, curiosité, sens de l?analyse, capable de documenter les processus et d?expliquer des concepts techniques à des non-spécialistes.
Bonus : expérience en IA générative, modèles de langage, NLP, computer vision ou gestion de projets IA complexes / MLOps / R&D.
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