Description

In this role, you will design and implement cutting-edge AI solutions on Azure, developing machine learning models and deploying intelligent applications. Your work will involve collaborating with cross-functional teams to ensure robust, scalable solutions that meet client needs and adhere to best practices.

Requirements

• •5+ years of hands-on experience in AI/ML engineering or data science roles
• •Fluency in German and English
• •Strong experience with Azure AI/Data ecosystem
• •Experience using Azure AI Foundry for building LLM-based applications
• •Proficiency in Python and common ML/DL libraries
• •Practical understanding of MLOps and model lifecycle management
• •Exposure to Large Language Models or generative AI technologies
• •Strong problem-solving and communication skills
• •Azure and data/AI certifications are highly regarded
• •Bachelor’s degree in Computer Science, Engineering, or related field

Tasks

• •Design and implement end-to-end AI solutions on Azure
• •Develop machine learning models, including predictive algorithms and anomaly detection
• •Integrate machine learning models into cloud applications
• •Write code in Python to build and train models
• •Leverage Azure Machine Learning and Azure Databricks as needed
• •Build and deploy intelligent applications using Azure AI Foundry
• •Utilize prompt orchestration and agent orchestration features for GenAI use cases
• •Independently implement GenAI workflows using Foundry
• •Develop Generative AI solutions for clients
• •Experiment with large language model applications using Azure OpenAI Service
• •Build conversational agents and Retrieval-Augmented Generation pipelines
• •Ensure robust and secure prompt engineering and fine-tuning
• •Utilize Azure Cognitive Services for vision, speech, and language tasks
• •Integrate Azure Cognitive Search for semantic search solutions
• •Use Form Recognizer in document processing pipelines
• •Combine Azure services with custom ML to meet client needs
• •Deploy and operationalize ML models
• •Containerize models or use Azure ML endpoints
• •Set up CI/CD pipelines for automated model training and deployment
• •Implement monitoring for model performance drift
• •Ensure AI solutions scale in production and follow DevOps best practices
• •Collaborate with Data Engineers to ensure reliable data for AI models
• •Contribute to data pipeline design for ML
• •Define feature engineering processes for model training
• •Work with Solution Architects to translate designs into implementation tasks
• •Collaborate with data scientists and cloud engineers on AI solutions
• •Participate in code reviews and knowledge sharing sessions
• •Support client teams in adopting AI solutions
• •Prepare technical documentation and demo functionalities
• •Iterate on models based on user feedback and requirements
• •Ensure AI solutions meet responsible AI guidelines and data privacy standards

Tools & Technologies

Benefits