Darum lohnt es sich
Ignition, AVEVA PI) using Azure IoT and Fabric Drive AI innovation across customer and commercial processes via Microsoft Dynamics, aligned with broader enterprise architecture Embed security, identity (Entra ID), privacy, and responsible AI controls into all solutions Lead and mentor a team of engineers and data scientists, reviewing designs and ensuring delivery excellence Manage MLOps, technical debt, and lifecycle to ensure scalable, maintainable, and cost‑efficient AI solutions Requirements 8+ years of experience in architecture/engineering with hands‑on delivery of AI, ML, or data solutions Deep expertise in Microsoft cloud and AI stack including Azure AI, Azure OpenAI, Microsoft Fabric, Power Platform, and M365/Copilot Strong coding and MLOps capabilities, including experience with RAG, vector databases, pipelines, and inference services Solid understanding of modern data architecture including lakehouse, real‑time and batch processing, and data governance Proven experience designing end‑to‑end AI solutions in complex enterprise environments Experience leading engineers and data scientists as a hands‑on player‑coach Strong ownership mindset with the ability to take initiatives end‑to‑end and deliver outcomes Experience working with OT/industrial data and edge‑to‑cloud analytics architectures Exposure to GxP or regulated environments (GAMP 5, 21 CFR Part 11, EU Annex 11) Experience with Microsoft Dynamics and AI‑driven customer data platforms Knowledge of Copilot Studio / Agent 365 governance and scaling enterprise AI agents Fluency in English; Spanish is an advantage #J-18808-Ljbffr Responsibilities Design, build, and deploy AI solutions hands‑on using Azure AI, Azure OpenAI, and Microsoft Fabric, taking use cases from prototype to production Own the end‑to‑end architecture of the Microsoft AI ecosystem, including M365 Copilot, Agent 365, and Power Platform Develop and manage the enterprise data and analytics platform (Microsoft Fabric) to support AI, RAG, and BI use cases Build and integrate edge‑to‑cloud data pipelines for OT and MES signals (e.g.
