Microsoft Cloud Solution Architect - Azure AI / Machine Learning 

United Kingdom, England 

380663793

Required/Minimum Qualifications:

• Bachelor’s degree in computer science, Information Technology, Engineering, Business or related field AND experience in cloud/infrastructure technologies, information technology (IT) consulting/support, systems administration, network operations, software development/support, technology solutions, practice development, architecture, and/or Business Applications consulting OR equivalent experience.

• Domain Expertise in Azure AI Areas:Deep domain expertise in one of the Azure AI specific areas, such as Cognitive Services, Machine Learning, Azure OpenAI and CoPilot OR hands-on experience working with the respective products at the expert level.

• Breadth of technical experience and knowledge in foundational security, foundational AI, architecture design, with depth / Subject Matter Expertise in one or more of the following:

• • Core AI & ML Concepts: Familiarity with AI & ML foundational knowledge of concepts like Prompt Engineering, compute systems (GPU & FPGA), popular frameworks (TensorFlow & PyTorch), and tools (Jupyter notebooks & VS Code).

• • Generative AI and Responsible AI: Knowledge of current and emerging AI technology, including Generative AI technology applications and use cases (including, but not limited to, Large Language Models) and Foundational models toolsets. Understanding of Responsible AI practice including ethical considerations, bias mitigation, and fairness.

• • Architecting Enterprise-Grade Solutions: The ability to create and explain 3-tier architecture diagrams, system context diagrams, system interaction diagrams, etc.

• • Proven experience building enterprise-grade, AI-focused solutions on the cloud (Azure, AWS, GCP) for customers, from Minimum Viable Products (MVPs) leading to production deployments.

• • Programming Languages and Integration: Proficient with Python, C#, R, JavaScript, or similar programming languages in the context of application development, and ability to integrate Azure AI with other services (e.g., Azure Functions, Kubernetes, Docker, API Management).

• • DevOps and MLOps: Strong understanding of DevOps practices and CI/CD tool chains, and familiarity with MLOps (AI & ML lifecycle management) for sustainable enterprise grade deployments.

• • Competitive Landscape: Understanding the competitive landscape is valuable, candidates should be aware of key AI platforms beyond Azure, such as AWS and GCP. Knowledge of the AI open-source ecosystem

• Understand customers'' overall data estate, business priorities, and IT success measures.
• Innovate with AI solutions that drive business value.
• Facilitate scalable delivery through strong technical programme management utilizing a factory model/approach; driving programme awareness and demand across the regional areas.
• Ensure Solution Excellence: Deliver solutions with high performance, security, scalability, maintainability, repeatability, reusability, and reliability upon deployment. Gather insights from customers and partners.

• Drive Consumption Growth: Develop opportunities to enhance Customer Success and help customers extract value from their Microsoft investments.
• Unblock Customer Challenges: Leverage subject matter expertise to identify resolutions for customer blockers. Follow best practices and utilize repeatable IP.
• Build repeatable IP and assets that create velocity in deployment and drives customer value from their Unified investment. Continuously look to improve upon these assets utilizing the best of field inputs.
• Architect AI Solutions: Apply technical knowledge to design solutions aligned with business and IT needs. Create Innovate with AI roadmaps, lead POCs and MVPs, and ensure long-term technical viability.

Technical Leadership:

• Advocate for Customers: Share insights and best practices, collaborate with the Engineering team to address key blockers, and influence product improvements, roadmap and feature prioritization.
• Continuous Learning: Stay updated on market trends, collaborate with the AI technical community, and educate customers about the Azure AI platform.
• Accelerate Outcomes: Through engaging with field teams, share expertise, contribute to IP creation, and promote reusability to accelerate customer success, as well as collate feedback on assets to drive improvement and leverage field teams inputs.