AI Integration Architect

• Design and implement end-to-end AI architectures, including data preparation, model development, deployment, and lifecycle management
• Drive the adoption of standards for AI/ML and GenAI within platform feature design
• Evaluate emerging AI technologies, frameworks, and tools, and recommend their integration into the platform
• Translate business and product needs into AI technical architecture requirements
• Produce AI architecture documentation (diagrams, guidelines, specifications), including responsible AI and governance aspects
• Provide technical leadership and guidance on AI topics to data engineering, analytics, and product teams
• Ensure performance, scalability, reliability, security, and ethical use of AI solutions
• Contribute to architecture reviews and ensure alignment with enterprise architecture, data, and AI strategies
• Promote best practices for MLOps, ModelOps, and AI lifecycle management
• Support the development of scalable, secure, and compliant AI capabilities within Data & AI platforms
• Ensure compliance with best practices, regulatory constraints, data privacy, and Responsible AI principles
• Enable reuse and industrialization of AI patterns, accelerators, and components across platforms

Key requirements:

• Minimum 6–8 years of experience in IT, including roles such as Integration Architect or Solution Architect
• Proven experience in AI/ML architecture and advanced analytics platforms
• Understanding of AI/ML architectures including feature stores, model training, deployment, and monitoring
• Strong understanding of cloud-based AI ecosystems and data platforms
• Expertise in Azure AI services (Azure Machine Learning, Azure OpenAI, Cognitive Services, Databricks ML/LLM features)
• Experience with MLOps frameworks and CI/CD for AI (Azure ML pipelines, GitOps, model versioning)
• Data processing and feature engineering using Azure Data Factory, Databricks, Spark
• Knowledge of security and governance for AI: data privacy, model access control, Responsible AI, explainability, and bias mitigation
• Experience with monitoring and observability for AI workloads (model performance, drift, quality, cost)
• Familiarity with GenAI patterns such as RAG, prompt engineering, and vector databases

Nice to have:

• Experience with frameworks such as LangChain, Semantic Kernel, or AutoGen
• Knowledge of Agentic AI / Autonomous Agents architecture
• Relevant certifications (TOGAF, Azure/AWS Architect, MuleSoft, etc.)
• German language skills