Microsoft Azure AI Engineer; m​/w​/d

Overview

Do you want to not only experience but actively shape the unique DNA of Microsoft
? Then Devoteam M
Cloud is the right place for you! Our core business is cloud-based enterprise customer projects, where we drive the most modern Microsoft technologies.

The building blocks that distinguish us:

• 2,100 Microsoft certifications completed in 2022
• 1,000 Microsoft experts in EMEA
• 600 customers
• 25 years of industry experience
• 18 Advanced Specializations - including "Azure Virtual Desktop Advanced Specialization" and "Modernization of Web Applications to Microsoft Azure"
• 6 Solution Partner Designations
• 3 MVPs

Imagine being part of one of the most successful IT companies in Europe, leveraging the latest technologies with its customers to elevate them to the next level. Apply now at Devoteam M Cloud!

• Modern offices in prime locations in Munich and Frankfurt
• Flexible hybrid working with the option to customize your working hours to your individual needs
• Our Devoteam Academy offers a wide range of certified trainings and language courses
• International development opportunities to boost your career at Devoteam
• Gaming lounge for your creative break between meetings and calls
• Get-together parties and team events for regular exchange and fun with your colleagues
• Employee referral bonuses for attracting new employees
• Modern IT equipment - choose the product that suits you best from a variety of options
• Corporate benefits with a large selection of numerous offers for almost every area
• "Jobrad" (company bike) offer with attractive tax advantages for you
• Company pension scheme, direct insurance, and capital-forming benefits are available to you as additional services
• Integration Day including mentoring programs for your perfect start at Devoteam

• 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 work spaces, 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 Spark
  
  SQL 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…