As a Senior Microsoft Azure AI Engineer (m/w/d) Lead AI Solution Architecture , you're responsible for the end-to-end architecture of AI/ML solutions on Azure . You provide technical leadership to teams in implementing best practices and MLOps, and work closely with clients to translate their business needs into innovative AI solutions.

Activities

• 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.

Requirements

• Proven Experience: 7+ years of experience in data analytics and software development, with at least 4-5 years in designing and implementing ML/AI solutions at scale. A track record of delivering production-grade AI projects for enterprise clients is essential (this is a senior role and not suitable for junior candidates ).
• Azure Expertise: Deep hands-on knowledge of Azure data and AI services - including Azure Machine Learning, Azure Databricks, Azure Data Lake/Synapse, Azure Cognitive Services (Text, Vision, Speech), Azure OpenAI and Azure AI Foundry. Ability to architect solutions that leverage these services cohesively.
• Architectural Skills: Strong skills in system design and integration. Comfortable defining solution architectures that encompass data ingestion, feature engineering, model training, deployment (APIs, containers), and monitoring. Familiarity with designing microservices or cloud data pipelines is a plus.
• MLOps & Software Engineering: Solid understanding of MLOps principles and experience implementing ML lifecycle management (source control, CI/CD for models, model registries, etc.) on Azure or similar platforms. Proficiency in Python and common ML frameworks (scikit-learn, TensorFlow/PyTorch) and experience with code review and DevOps processes.
• Leadership & Communication: Excellent leadership and interpersonal skills. Able to interface with client stakeholders to explain complex AI concepts in business terms, gather requirements, and drive adoption. Experience leading technical teams or mentoring engineers in a project setting.
• AI Knowledge: Broad knowledge of machine learning and AI techniques (supervised, unsupervised learning, time-series, etc.) and familiarity with deep learning and NLP. Exposure to Generative AI and LLMs is highly desired - you should understand concepts like prompt engineering and have curiosity about applying these in enterprise scenarios. Deep understanding of Azure AI Foundry concepts (e.g., grounding, orchestration, Azure Agents) and ability to evaluate where it fits in a solution landscape.
• Certifications: Relevant certifications are a strong plus, demonstrating your expertise in Azure and AI. Examples include Microsoft Certified: Azure Solutions Architect Expert and Azure AI Engineer Associate (AI-102). Certification in machine learning frameworks or platforms (e.g. Databricks Certified Generative AI Engineer Associate ) is also valued. Devoteam supports continuous learning and certification attainment.

Team

You will be part of a collaborative, remote-friendly team that values continuous learning and
delivering impact through modern cloud-native data solutions.

Application Process

1. After you apply, a recruiter will invite you to an initial get-to-know-you session, where you'll learn more about Devoteam Germany and the position. This conversation is an opportunity for us to learn more about you and your professional goals, and also to introduce you to Devoteam.
2. Following that, our tech experts look forward to a technical discussion with you. In this conversation, you'll get to know our technologies and team structure.
3. If everything aligns for both you and us, we'll extend an offer to you within 48 hours.