Description

As an Azure AI Engineer, you will develop and implement AI/ML models to address business challenges, deploy solutions on Azure, and collaborate with a cross-functional team. This role involves data preparation, model tuning, and integrating AI services to enhance project outcomes.

Requirements

• •3–6 years of experience developing AI or machine learning solutions
• •Fluency in German and English
• •Proficiency in Python; familiarity with R or C# is a plus
• •Solid understanding of machine learning algorithms and workflows
• •Experience with Scikit-learn; exposure to TensorFlow or PyTorch is nice to have
• •Hands-on experience with Azure cloud services
• •Experience with Azure Machine Learning, Databricks, or HDInsight
• •Comfortable working with databases and writing SQL
• •Experience in handling data pipelines or ETL processes
• •Exposure to Azure AI Foundry is a plus
• •Strong analytical thinking and problem-solving ability
• •Good communication and teamwork skills
• •Proactive about learning new tools and techniques
• •Bachelor’s degree in Computer Science, Engineering, Mathematics, or related field
• •Willingness to pursue relevant certifications

Tasks

• •Develop and implement AI/ML models
• •Train predictive models for business problems
• •Create natural language solutions like document classifiers and chatbots
• •Use Azure Machine Learning for experiment tracking and model management
• •Write code in Python and SQL for data processing and model training
• •Deploy AI solutions on Azure cloud infrastructure
• •Set up Azure ML workspaces and Azure Functions for hosting models
• •Create Azure Data Factory pipelines for data movement
• •Ensure deployment follows security and compliance guidelines
• •Collaborate with data engineers to gather and prepare datasets
• •Contribute to data preprocessing and write transformation scripts
• •Define features and handle data quality issues
• •Utilize PySpark and SparkSQL for large-scale data processing
• •Conduct experiments to improve model performance
• •Tune hyperparameters and evaluate model results
• •Leverage Azure ML pipelines to automate training runs
• •Integrate Azure Cognitive Services for pre-built AI capabilities
• •Implement MLOps practices for model management
• •Create release pipelines and set up monitoring for performance
• •Document work thoroughly for maintainability and knowledge transfer
• •Collaborate in a cross-functional team and participate in design discussions

Tools & Technologies

Benefits