Senior Microsoft Azure AI Engineer; m​/w​/d

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!

Your Benefits:

• 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

• 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 Lang Chain 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 summarisation 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 containerise models or use Azure ML endpoints, set up CI/CD pipelines (using Azure Dev Ops or Git Hub 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 Dev Ops 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…