Solutions Architect

• Bachelor's or Master's degree in Computer Science, Information Systems, Data 
  Science, or a related field.  
• Minimum of 8 years of experience in cloud architecture, defining end to end 
  ecosystems to support advanced analytics and AI solutions.  
• Relevant Azure certifications (e.g., Azure Solutions Architect Expert, Azure Data  Engineer Associate) are a plus.  
• Proven experience as a solutions architect in a cloud environment. 

Azure Data and AI Ecosystem:  

• Design, implement, and integrate Azure services such as Azure Data Fabric, Azure
• Data Factory, OneLake, Purview Unified Catalog, AI Foundry, Machine Learning, 
  Cognitive Services, Power BI, Power Automate, and application services to deliver 
  high quality data and AI products to our members.  
• Generate and spearhead the execution of a multi-year implementation roadmap for 
  the Azure data and AI ecosystem that includes individual components and their 
  integration to create a continuum to support AF's AI-powered solutions.  
• Optimize the Azure data and AI ecosystem to iteratively support growing 
  complexity and more sophisticated operations as new use cases and AI solutions 
  are identified and delivered.  
• Ensure that Azure-based data and AI solutions include embedded metrics to 
  understand performance and aid in maintenance and support activities. - Operate 
  and administer AF's data and AI Azure ecosystem to ensure that best practices are 
  utilized and alignment of AF's operating, security and compliance polices are 
  observed.  
• Manage the Azure data and AI ecosystem costs, working with stakeholders to 
  identify and set budgets for Azure services, and monitoring consumption and 
  licensing for Azure data and AI products and services.  
• Orchestrate the utilization of Azure AI suite of products and services to support 
  best practices in the development, testing, integration, and deployment of AI 
  models and algorithms. 
• Coordinate with data engineers and teams, AI engineers and developers, 
  application and infrastructure teams, and data governance and security teams to 
  ensure that deployed AI-powered solutions are ready for hand off to support teams, 
  ensuring knowledge transfer is adequate for the successful maintenance and 
  support of AI solutions.  
• Identify and resolve technical issues related to cloud infrastructure and 
  applications, providing timely support and solutions.