Azure AI Solutions Architect

The ideal candidate will have extensive Microsoft/Azure data services and Azure OpenAI technology experience. The role requires a combination of technical expertise, leadership skills and industry knowledge.  Candidates will be expected to contribute to all stages of the data lifecycle. 

1. Technical Proficiency: 
2. Strong background in artificial intelligence, machine learning, and data science. 
3. Proficiency in Microsoft AI technologies, including Azure Machine Learning, Azure Cognitive Services, and related tools. 
4. Leadership Skills: 
5. Proven experience in leading and managing teams, especially in the AI consulting domain. 
6. Strategic thinking and the ability to align AI initiatives with overall business goals. 
7. Effective communication and collaboration skills to work with cross-functional teams and clients. 
8. Project Management: 
9. Experience in successfully managing AI projects from conception to delivery. 
10. Familiarity with project management methodologies, such as Agile or Scrum. 
11. Client Relationship Management: 
12. Ability to understand client needs and deliver AI solutions that meet their requirements. 
13. Strong interpersonal skills to build and maintain client relationships. 
14. Problem-Solving and Analytical Skills: 
15. Critical thinking and problem-solving skills to address complex AI challenges. 
16. Analytical mindset for data-driven decision-making. 
17. Innovation and Adaptability: A forward-thinking mindset with a focus on innovation in AI technologies. Ability to adapt to evolving industry trends and incorporate new technologies into consulting services. 
18. Educational Background: 
    A relevant educational background, such as a degree in computer science, data science, or a related field. 
    Certifications in Microsoft AI technologies can be advantageous. 
19. Team Collaboration: 
    Ability to foster a collaborative and inclusive team culture. 
    Experience in mentoring and developing team members. 
20. Ethical Considerations: 
    Awareness of ethical considerations in AI and a commitment to responsible AI practices. 
21. Results-Oriented: 
    A track record of delivering successful AI projects on time and within budget. 
    Metrics-driven approach to measure the impact of AI solutions.