Microsoft Cloud Solution Architect Data AI 

Taiwan, Taoyuan City 

60268189

You will also drive product influence with Engineering through technical feedback via the Factory and increase technical intensity with the Field teams. This opportunity will allow you to accelerate your career growth, honing your technical and program management skills, and deepening your cloud expertise.

Additional or Preferred Qualifications

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:

• Deep Domain Expertise in Azure Analytics & AI Areas: Domain expertise in one of the Azure Analytics & AI specific areas OR hands-on experience working with the respective products at the expert level.
• Programming Languages and Integration: Knowledge 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, Azure Container Apps, Docker, API Management). Also, enterprise-scale technical experience and depth with one or more areas of the Azure Analytics ecosystem (Microsoft Fabric, Power BI, Power BI embedded, Azure Analysis Services, Azure Databricks, Azure Data Lake)
• 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.
• Infrastructure as Code (IaC) Deployment: Understanding of Bicep, Terraform, or Azure Resource Manager and familiarity with configuration and deployment of IaC templates in a secure environment. · Core AI & ML Concepts: Familiarity with AI & ML foundational knowledge of concepts like Prompt Engineering, 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.
• 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

Customer-Centric Approach:

• Play a pivotal role in the Analytics & AI Factory, providing technical enablement, operational support, and strategic engagement across customer projects. This role is designed to ensure successful delivery of AI solutions, troubleshoot complex issues, and support innovation through pilot evaluations and advanced use case development. 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 program management utilizing a factory model/approach, driving program awareness and demand across the regional operating units. Attend in-flight project status meetings to monitor progress and identify support needs. Engage directly with complex or non-standard customer use cases beyond existing accelerators. Participate in intake reviews for milestone sizing, objection handling, and technical scoping.
• Ensure Solution Excellence: Deliver solutions with high performance, security, scalability, maintainability, repeatability, reusability, and reliability upon deployment. Gather insights from customers and partners.

Business Impact:

• Drive Consumption Growth: Develop opportunities to enhance Customer Success and help customers extract value from their Microsoft investments.
• Operational Awareness: Understanding of consumption metrics and their reflection on program effectiveness. Ability to collaborate with local teams for nominations and intake support.
• Unblock Customer Challenges: Leverage subject matter expertise to identify resolutions for customer blockers. Follow best practices and utilize repeatable IP.
• Identify and contribute 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. Evaluate pilot opportunities for potential integration into existing offerings.
• 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 Factory team to address key blockers, and influence improvements, roadmap and feature prioritization.
• Resolve technical blockers: Debug technical issues and provide fixes for engagements encountering blockers.
• Continuous Learning: Stay updated on market trends, collaborate with the Analytics & AI technical community, and educate customers about the Azure AI platform.
• Accelerate Outcomes: Through engaging with field teams, share expertise, drive factory pipeline, and promote Factory to accelerate customer success, as well as collate feedback upon execution to drive improvement and leverage field teams inputs.