Microsoft Azure AI Engineer (m/w/d)

• Build AI/ML Solutions: Develop and implement AI/ML models to solve specific business problems. This could range from training predictive models (for example, forecasting equipment failures in manufacturing or customer behavior in insurance) to creating NL solutions like document classifiers or chatbots. You will use Azure Machine Learning for experiment tracking and model management, and write code (Python/SQL) for data processing and model training.
• Azure Cloud Implementation: Deploy AI solutions on Azure cloud infrastructure. Set up necessary resources such as Azure ML workspaces, Azure Functions or AKS for hosting models, and Azure Data Factory pipelines for data movement. Ensure that the deployment is done following Devoteam’s and Microsoft’s guidelines for security and compliance (important for enterprise clients in regulated industries like pharma and finance).
• Data Preparation & Feature Engineering: Work with data engineers to gather and prepare datasets required for machine learning. You will contribute to data preprocessing steps, writing data transformation scripts, defining features, handling data quality issues, to ensure models are trained on high-quality data. Utilize PySpark and SparkSQL in Azure Databricks or Microsoft Fabric for large-scale data processing when needed.
• Experimentation & Model Tuning: Conduct rigorous experiments to improve model performance. This involves trying out different algorithms or model architectures, tuning hyperparameters, and evaluating results. You’ll leverage Azure ML pipelines to automate training runs and compare metrics. For instance, you might compare a classical machine learning approach with a deep learning approach for a given problem and choose the best-performing model.
• Integration of AI Services: Make use of Azure’s AI services to accelerate development. Incorporate pre-built AI capabilities via Azure Cognitive Services (e.g. language translation, OCR, sentiment analysis) when appropriate instead of building from scratch. For example, if a project requires extracting text from PDFs and analyzing sentiment, you might use Azure Form Recognizer and Text Analytics as part of the solution before applying a custom model.
• MLOps & Monitoring: Implement the basics of MLOps for the solutions you build. Register models, create release pipelines for deploying them, and set up monitoring (for both system performance and model accuracy drift over time). Ensure that logging and alerting are in place so that any issues in production (like an API downtime or data drift causing model degradation) can be quickly identified and addressed.
• Collaboration & Documentation: Collaborate within a crossfunctional team, taking guidance from the Senior AI Engineer and Architect while also providing input based on your own expertise. Participate in design discussions, sprint planning, and code reviews. Additionally, document your work (datasets used, model assumptions, API specs) thoroughly to aid maintainability and knowledge transfer within the team and to clients’ IT teams.

• Experience: Approximately 3–6 years of experience developing AI or machine learning solutions. You should have real-world project experience beyond internships – for example, having built predictive models or data-driven applications that were deployed or used in a production environment.
• Language Skills: fluent in german and english
• Technical Skills: Proficient in programming (Python required; familiarity with another language like R or C# is a plus). Solid understanding of machine learning algorithms and workflows (data preprocessing, feature selection, model training, evaluation). Experience with frameworks such as Scikit-learn; exposure to deep learning frameworks (TensorFlow, PyTorch) is nice to have.
• Azure Familiarity: Hands-on experience with Azure cloud services, especially those relevant to data and AI. This could include having used Azure Machine Learning for training/deploying models, Azure Databricks or HDInsight for big data, and Azure Storage/Azure SQL for data. If you have experience with Azure Cognitive Services or Azure OpenAI, mention it – it’s advantageous as we frequently use these in projects.
• Data Engineering Basics: Comfortable working with databases and writing SQL. Experience in handling data pipelines or ETL processes is important since ML engineers in our team often need to fetch and prepare data. Knowledge of Azure Data Factory or Synapse pipelines is a plus.
• Azure AI Foundry: Exposure to Azure AI Foundry is a plus.
• Problem Solving: Strong analytical thinking and problem-solving ability in the context of debugging models and data issues. For example, you should be able to investigate why a model is underperforming (inspecting data imbalance, feature issues, etc.) or why a pipeline failed, and then propose fixes.
• Team Player: Good communication and teamwork skills. You’ll be part of an agile project team, so being able to discuss ideas, ask questions, and share findings with colleagues (developers, architects, project managers) is key. Ability to clearly document and explain your work to others (including client technical teams) is required.
• Continuous Learner: Given that Devoteam is building up its AI capabilities, we value engineers who are proactive about learning new tools and techniques. You should demonstrate an interest in staying up-to-date with AI innovations (for instance, new developments in LLM frameworks or MLOps tools). We encourage and support learning, so a mindset of continuous improvement will fit well here.
• Education & Certification: Bachelor’s degree in Computer Science, Engineering, Mathematics, or related field. In lieu of a degree, substantial relevant experience will be considered. Certifications like Azure AI Engineer Associate (AI-102) or Azure Data Engineer show your commitment to excellence and are a plus – if you haven’t obtained them yet, you should be willing to pursue certification with our support. Any certification or coursework in data science/ML (e.g. Microsoft DP-100 exam or Databricks ML certificates) is also beneficial.

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