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(MID)\nIlość poszukiwanych osób: 1\nZakres obowiązków:\nTworzenie i utrzymanie platformy AI w branży ubezpieczeniowej:\nProjektowanie i budowa infrastruktury MLOps/LLMOps: Tworzenie skalowalnego środowiska do trenowania i serwowania modeli przy użyciu Azure Machine Learning, Azure AI Foundry oraz Kubernetes (AKS).\nAutomatyzacja procesów CI/CD/CT: Implementacja potoków (pipelines) CI/CD dla rozwiązań ML, obejmujących automatyczne testowanie, wersjonowanie danych i modeli (DVC, MLflow) oraz Continuous Training (CT).\nKonteneryzacja i orkiestracja: Przygotowywanie obrazów Docker dla modeli AI/GenAI oraz zarządzanie ich wdrożeniami na klastrach Kubernetes w architekturze hybrydowej (integracja z systemami on-premise).\nMonitoring i Observability: Wdrożenie zaawansowanego monitoringu modeli (wykrywanie Data Drift/Model Drift), logowania i alertowania, aby zapewnić wysoką dostępność usług AI.\nWsparcie techniczne dla AI Act: Implementacja narzędzi do audytowalności modeli, lineage (śledzenie pochodzenia danych) oraz bezpieczeństwa (zarządzanie dostępem, szyfrowanie) zgodnie z wymogami regulacyjnymi.\nOferta:\nLokalizacja: Warszawa/ Wola– praca hybrydowa – 1x w tygodniu w biurze\nStawka: do 170zł netto/ h\nWspółpraca: długofalowa\n\nKogo poszukujemy?\nWymagania:\nMinimum 3 lata doświadczenia w obszarze DevOps, MLOps lub Inżynierii Oprogramowania, w tym praktyka w pracy z modelami ML na produkcji.\nBiegłość w konteneryzacji i orkiestracji: Zaawansowana znajomość Docker i Kubernetes (zarządzanie klastrami, Helm charts, Ingress).\nDoświadczenie z chmurą Publiczną: Głęboka znajomość Azure (w szczególności Azure ML, AKS, Azure Container Registry) lub GCP/AWS z gotowością do szybkiego wejścia w Azure.\nPraktyka w CI/CD: Doświadczenie w budowaniu pipeline'ów (Azure DevOps, GitHub Actions, Jenkins) uwzględniających specyfikę ML (np. trenowanie modelu jako krok w pipeline).\nProgramowanie i skryptowanie: Dobra znajomość Python (niezbędna do pracy z SDK narzędzi ML) oraz 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