Senior Microsoft Azure AI Engineer (m/w/d)

AI Solution Development: Design and implement end-to-end AI solutions on Azure. This includes developing machine learning models (e.g. predictive algorithms, classification, anomaly detection) and integrating them into cloud applications. You will write code (primarily in Python) to build and train models, leveraging Azure Machine Learning, Azure Databricks, or custom frameworks as needed.Build and deploy intelligent applications using Azure AI Foundry, leveraging prompt orchestration, grounding with Azure AI Search, and agent orchestration features for enterprise-scale GenAI use cases. Independently implement GenAI workflows using Foundry.Generative AI & LLMs: Play a key role in developing Generative AI solutions for clients. Experiment with and implement large language model (LLM) based applications using Azure OpenAI Service and frameworks like LangChain for building conversational agents or Retrieval-Augmented Generation (RAG) pipelines. For example, you might build a chatbot that uses an LLM with enterprise data or create a summarization service for insurance documents. Ensure prompt engineering and fine-tuning are done in a robust, secure manner.Azure AI Services Integration: Utilize Azure Cognitive Services (e.g. for vision, speech, language) and Azure AI services to speed up solution development. For instance, integrate Azure Cognitive Search for semantic search in a knowledge retrieval solution, or use Form Recognizer in a document processing pipeline. Combine these services with custom ML where appropriate to meet client requirements.MLOps and Deployment: Take ownership of deploying and operationalizing ML models. You will containerize models or use Azure ML endpoints, set up CI/CD pipelines (using Azure DevOps or GitHub Actions) for automated model training and deployment, and implement monitoring for model performance drift. Ensure that the AI solutions can scale in production and adhere to DevOps best practices for reliability.Data Pipeline Collaboration: Work closely with Data Engineers to ensure the data needed for AI models is available, reliable, and well-prepared. Contribute to data pipeline design for ML – e.g., help define feature engineering processes or streaming data ingestion for real-time inference – so that models can be trained on and serve high-quality data.Cross-Functional Collaboration: Collaborate with Solution Architects (like the ML/AI Architect) to translate high-level architectural designs into concrete implementation tasks. Work alongside other specialists such as data scientists and cloud engineers to build secure, end-to-end AI solutions that integrate into the client’s ecosystem. This includes participating in code reviews, knowledge sharing, and troubleshooting sessions within the team.Client Support & Iteration: Support client teams in adopting and understanding the AI solutions. This could involve preparing technical documentation, demoing functionalities to stakeholders, and iterating on models based on user feedback or changing requirements. Ensure that AI solutions meet responsible AI guidelines and data privacy/security standards as expected by enterprise clients.