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ubezpieczeniowej\nProjektowanie i budowa infrastruktury MLOps/LLMOps: skalowalne środowisko do trenowania i serwowania modeli (Azure Machine Learning, Azure AI Foundry, AKS)\nImplementacja pipeline'ów CI/CD/CT dla rozwiązań ML: automatyczne testowanie, wersjonowanie danych i modeli (DVC, MLflow), Continuous Training\nPrzygotowywanie obrazów Docker dla modeli AI/GenAI oraz zarządzanie ich wdrożeniami na klastrach Kubernetes w architekturze hybrydowej (integracja z systemami on-premise)\nWdrożenie monitoringu modeli (wykrywanie Data Drift/Model Drift), logowania i alertowania w celu zapewnienia wysokiej dostępności usług AI\nWsparcie techniczne zgodności z AI Act: narzędzia do audytowalności modeli, data lineage oraz bezpieczeństwa (zarządzanie dostępem, szyfrowanie)\nOptymalizacja kosztów i wydajności: zarządzanie zasobami Azure, optymalizacja czasu inferencji modeli, skalowanie infrastruktury w zależności od obciążenia\nWymagania:\nMinimum 3 lata doświadczenia w obszarze DevOps, MLOps lub inżynierii oprogramowania, w tym praktyka w pracy z modelami ML na produkcji\nZaawansowana znajomość Docker i Kubernetes (zarządzanie klastrami, Helm charts, Ingress)\nGłęboka znajomość Azure (w szczególności Azure ML, AKS, Azure Container Registry) lub GCP/AWS z gotowością do szybkiego wejścia w Azure\nDoświadczenie w budowaniu pipeline'ów CI/CD (Azure DevOps, GitHub Actions, Jenkins) uwzględniających specyfikę ML (np. trenowanie modelu jako krok w pipeline)\nDobra znajomość Python (praca z SDK narzędzi ML) oraz Bash/Shell\nPraktyczna obsługa MLflow, Kubeflow lub natywnych rozwiązań chmurowych do zarządzania cyklem życia modelu\nZnajomość Terraform, Bicep lub Ansible\nWykształcenie wyższe techniczne (informatyka, telekomunikacja lub pokrewne)\nPodejście \"Automation First\", dążenie do eliminacji pracy manualnej poprzez skrypty i narzędzia\nUmiejętność pracy na styku zespołów Data Science i IT Operations\nProaktywność w rozwiązywaniu problemów wydajnościowych i incydentów produkcyjnych\nŚwiadczenie usług z terytorium Polski\nMile widziane:\nCertyfikaty Azure: DevOps Engineer Expert (AZ-400) lub Azure AI Engineer (AI-102)\nDoświadczenie we wdrażaniu modeli LLM i architektur RAG\nZnajomość narzędzi do monitoringu (Prometheus, Grafana, Azure Monitor)\nRozumienie zagadnień sieciowych w chmurze hybrydowej (VPN, VNet, Private Endpoints)\nZnajomość baz wektorowych (np. w kontekście Azure AI Search)","description_format":"text","description_chars":2803,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[{"language":"Polish","level":"Advanced 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