Senior Cloud Solution Architect

Customer-Centric Approach: Play a pivotal role in the 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. Evaluate pilot opportunities for potential integration into existing offerings. Architect AI Solutions: Apply technical knowledge to design solutions aligned with business and IT needs. Ability to collaborate with local teams for nominations and intake support. 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 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. Bachelor's degree in computer science, Information Technology, Engineering, Business or related field AND 4+ years' 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. 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: Expertise with Azure AI Search and/or Vector Indexes, Azure Document Intelligence, Azure Content Understanding and /or equivalent OCR technology 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, Azure Container Apps, Docker, API Management, MCP servers). 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: Strong 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, RAG, Agentic Orchestration, tools (Jupyter notebooks & VS Code). GenAIOps & DevOps: Familiarity with CI/CD pipelines (GitHub Actions, Azure DevOps), Prompt and Model Evaluation, AI Harnesses (e.g., evaluation, orchestration), and observability for general Azure workloads using Application Insights, Azure Monitor, OpenTelemetry and/or other Observability tools. Agentic Workflows: Familiarity with Agent Framework, MCP, Langchain, or other agentic frameworks. Ability to configure tools for dynamic execution by an LLM 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