Lead Azure AI Solution Architect

Primoris Services Corporation • Texas, TX

18d • Remote

About The Position

We're seeking a Lead Azure AI Solution Architect to design and deliver enterprise AI solutions on Microsoft Azure and the Microsoft AI platform. This senior, hands-on role translates business objectives into well-architected AI capabilities, defines implementation patterns, leads PoCs and reference implementations, and guides engineering teams.

Requirements

• Bachelor’s degree in computer science, Information Technology, or a related field.
• 10+ years of progressive software engineering experience, with at least 5 years in a solution architecture capacity.
• Demonstrated experience designing and delivering cloud-based solutions.
• Microsoft Certified: Azure AI Engineer Associate (AI-102)
• Hands-on Azure solution design experience with Azure OpenAI, Azure AI Search, Azure AI Foundry, App Service, Functions, Logic Apps, Cosmos DB, and Entra ID.
• Experience designing generative and agentic AI patterns, including RAG, prompt engineering, multi-agent systems, orchestration, and tool/function calling.
• Strong C#/.NET architecture background with working knowledge of Blazor or React.
• Strong data architecture skills across SQL Server, Cosmos DB, MongoDB, and vector stores for AI workloads.
• Proficiency with Azure DevOps, YAML pipelines, and modern CI/CD practices.
• Strong executive communication skills, with a track record of presenting technical recommendations to senior leadership and translating between business and engineering audiences.
• Demonstrated ability to mentor engineers and influence technical direction across cross-functional teams.
• Strong understanding of SOLID principles, Clean Architecture, design patterns, API design, and cloud-native application design.

Nice To Haves

• Master’s degree in computer science or a related field.
• Microsoft Certified: Azure Developer Associate (AZ-204).
• Experience with Microsoft Agent Framework and .NET agent orchestration.
• Experience implementing MCP or Agentic RAG in production.
• Experience fine-tuning and deploying AI models.
• Working knowledge of Python for AI/ML prototyping.
• Experience with Bicep or infrastructure-as-code.
• Experience partnering with security and architecture teams in regulated or critical-infrastructure environments.

Responsibilities

• Lead AI solution architecture.
• Design architectures for generative and agentic AI initiatives, including RAG patterns, multi-agent systems, and AI-enabled business applications.
• Produce reusable patterns for engineering teams.
• Architect AI workloads on Azure.
• Design solutions using Azure OpenAI, Azure AI Search, Azure AI Foundry, App Service, Functions, Logic Apps, Cosmos DB, and supporting services.
• Partner with security to align solutions with approved standards.
• Define implementation patterns.
• Establish design standards and quality bars for AI implementations using SOLID principles, Clean Architecture, appropriate Gang of Four patterns, and cloud-native practices.
• Conduct architecture reviews and author ADRs.
• Build capability-focused PoCs.
• Prototype AI capabilities to validate feasibility, demonstrate patterns, de-risk delivery, and create examples engineers can extend into production.
• Partner with stakeholders.
• Translate business objectives into AI architectures, feasibility assessments, and solution-fit recommendations.
• Present recommendations to senior leadership in business-relevant language.
• Evaluate AI tooling.
• Assess AI frameworks, agent libraries, and integration tooling for functional fit, technical capability, and enterprise suitability.
• Guide Engineering implementation.
• Provide direction on C#/.NET architecture, frontend patterns (Blazor, React), API design, and data architecture across SQL Server, Cosmos DB, and vector stores.
• Mentor engineering talent.
• Mentor engineers, support technical interviews, calibrate hiring decisions, and help grow the AI engineering function.
• Maintain technical currency.
• Track Azure AI and generative/agentic AI advances, including Model Context Protocol, agentic RAG, and fine-tuning.
• Apply emerging capabilities when they deliver business value.
• Establish LLMOps practices.
• Define approaches for prompt/version management, RAG quality testing, model and prompt evaluation, telemetry, feedback loops, and deployment governance.
• Support production readiness.
• Guide AI solutions from PoC to production by addressing observability, evaluation, latency, cost, resiliency, monitoring, and supportability.

Benefits

• 401(k) with employer match
• Health, dental, and vision insurance
• Paid time off and 10 paid holidays
• Employee stock purchase plan
• Remote work flexibility

What This Job Offers

• Job Type: Full-time
• Career Level: Senior