Azure AI Expert

As an Azure AI Expert, you will be responsible for designing, developing, and implementing artificial intelligence (AI) and machine learning (ML) solutions using Microsoft Azure. You will work closely with cross-functional teams to understand business requirements, identify opportunities for AI implementation, and deliver innovative solutions that leverage Azure AI services. Your role will involve applying data science techniques, building and training ML models, and deploying AI solutions on the Azure platform. Responsibilities: 1. AI Solution Design: Collaborate with stakeholders to understand business needs and identify opportunities for AI adoption. Design Azure-based AI solutions that address those needs and leverage Azure AI services effectively. 2. Machine Learning Development: Develop ML models and algorithms using Azure Machine Learning, Python, and other relevant tools. Preprocess and analyze data, train models, and evaluate their performance. Incorporate feature engineering and model optimization techniques. 3. Azure AI Service Integration: Utilize Azure AI services, such as Azure Cognitive Services (e.g., speech recognition, natural language processing, computer vision) and Azure Machine Learning, to build intelligent applications. Integrate AI capabilities into existing systems or develop new AI-driven applications. 4. Data Processing and Analysis: Gather, preprocess, and analyze data from various sources to generate insights and create training datasets for ML models. Ensure data quality and integrity throughout the AI solution lifecycle. 5. Model Deployment and Scalability: Deploy ML models on Azure and optimize them for scalability and performance. Implement containerization techniques using Azure Kubernetes Service (AKS) or other relevant technologies. 6. Experimentation and Iterative Improvement: Conduct experiments and A/B testing to evaluate the effectiveness of AI models and solutions. Continuously refine and improve models based on feedback and real-world performance. 7. AI Infrastructure and DevOps: Set up and manage AI infrastructure on Azure, including resource provisioning, monitoring, and security. Implement DevOps practices to automate ML model deployment, monitoring, and retraining. 8. Collaboration and Documentation: Collaborate with cross-functional teams, including data scientists, engineers, and stakeholders, to ensure successful implementation and deployment of AI solutions. Document solution design, processes, and best practices. Qualifications and Skills: • Strong experience in designing, developing, and implementing AI solutions using Microsoft Azure. • Proficiency in machine learning techniques, algorithms, and frameworks (e.g., scikit-learn, TensorFlow, PyTorch). • Hands-on experience with Azure AI services, such as Azure Cognitive Services, Azure Machine Learning, and Azure Databricks. • Knowledge of data preprocessing, feature engineering, and model evaluation techniques. • Strong programming skills in Python and familiarity with relevant libraries and tools (e.g., pandas, numpy, JupyterNotebook). • Understanding of cloud infrastructure, particularly Azure services, and ability to deploy and manage AI solutions on Azure. • Experience with containerization technologies (e.g., Docker, Kubernetes) and their application in AI model deployment. • Familiarity with DevOps practices and tools for ML model deployment and automation (e.g., Azure DevOps, Git). • Excellent problem-solving, analytical thinking, and communication skills. • Ability to work collaboratively in cross-functional teams and effectively communicate complex AI concepts to non-technical stakeholders. Relevant Microsoft Azure certifications, such as Azure AI Engineer Associate, are a plus.