Senior Microsoft Azure AI/ML Architect (m/w/d)

Lead AI Solution Architecture: Own the end-to-end architecture for AI/ML solutions on Azure, from concept and design to deployment. Develop high-level solution designs that integrate with clients’ existing data platforms and infrastructure.Client Engagement: Work closely with enterprise clients to understand business challenges and identify opportunities where AI/ML can drive value (e.g. predictive maintenance in manufacturing, drug discovery insights in pharma, or risk modeling in insurance). Translate these needs into solution roadmaps and technical plans.Technical Leadership: Provide hands-on technical leadership to delivery teams. Guide Azure AI Engineers and Data Engineers in implementing best practices for data preparation, model development, and cloud deployment. Mentor team members in advanced AI techniques and review designs/code to ensure quality.MLOps & Best Practices: Establish and enforce MLOps best practices for the team, including reproducible workflows, continuous integration/continuous delivery (CI/CD) for ML models, automated testing, and monitoring of model performance in production. Ensure that solutions are scalable and maintainable over time.Innovation & Generative AI: Stay abreast of the latest AI trends and Azure services. Evaluate new technologies – from Azure Cognitive Services and Azure OpenAI to emerging open-source frameworks for LLMs (Large Language Models) and RAG (Retrieval-Augmented Generation). Incorporate generative AI capabilities where relevant to enhance client solutions (e.g. intelligent document processing with GPT models).Cross-Project Impact: Oversee and provide guidance on multiple AI projects in parallel, ensuring architectural consistency and reuse of best practices across engagements. Act as the go-to expert for solving complex technical problems and making high-level design decisions.Internal Capability Building: Contribute to Devoteam’s internal Data & AI capability. Develop reusable architecture blueprints, accelerators, and reference implementations for AI on Azure. Lead knowledge-sharing sessions and training to upskill colleagues, and support the growth of a community of practice around AI/ML.