AI Solution Architect

What You Will Do 

• Define AI standards, reference architectures, and reusable design patterns that accelerate consistent, high-quality solution delivery. 

• Design scalable, secure, and cost-efficient AI architectures and integration strategies. 

• Design enterprise AI solutions and agentic architectures, including RAG capabilities, voice-based agents, intelligent automation, multi-agent systems, orchestration patterns, and Human-in-the-Loop processes. 

• Guide architecture decisions across Microsoft AI technologies such as Copilot Studio, Azure AI Foundry, Azure OpenAI, and modern AI development frameworks. 

• Remain hands-on through prototyping, proof-of-concepts, technical reviews, and key implementation decisions, defining approaches for MVP delivery, production deployment, and solution scaling. 

• Define AI testing, evaluation, monitoring, quality assurance, and success measurement approaches. 

• Collaborate with governance, security, and risk teams to ensure compliance with enterprise standards and responsible AI requirements. 

• Support solution shaping, technology selection, effort estimation, and technology adoption decisions, including evaluation of emerging AI capabilities and platforms. 

• Mentor team members and help grow AI engineering capability across the organization. 

What We Are Looking For 

Required Experience 

• 8+ years of experience in software engineering, solution architecture, or enterprise application development. 

• 4+ years of experience designing and delivering AI, GenAI, intelligent automation, or machine learning solutions. 

• Strong experience with Copilot Studio, Microsoft Foundry, Azure OpenAI, and Azure AI Search. 

• Strong understanding of Copilot Studio and enterprise AI solution design. 

• Solid experience with the Azure platform, including application hosting, integration, security, identity, networking, storage, and data services. 

• Proficiency in Python or other programming languages and experience with modern software development practices. 

• Experience designing and integrating REST APIs and enterprise applications. 

• Hands-on experience with SQL and relational databases. 

• Experience with GitHub and source control best practices. 

• Strong understanding of LLMs, Generative AI, prompt engineering, vector search, embeddings, and RAG architectures. 

• Experience with agent-based architectures, orchestration frameworks, and Human-in-the-Loop solutions. 

• Knowledge of AI evaluation methods, quality measurements, monitoring, and observability practices. 

• Understanding of Responsible AI, AI governance, and AI cost optimization principles. 

• Strong understanding of AI-assisted software development practices, including the effective use of tools such as M365 Copilot, Cursor AI, Claude Code, and similar AI-powered engineering assistants. 

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​Decision-Making Capabilities 

You should be comfortable making architecture decisions across: 

• Low-code vs pro-code approaches. 

• Copilot Studio vs custom AI solutions. 

• Single-agent vs multi-agent architectures. 

• Human-in-the-Loop vs autonomous workflows. 

• Trade-offs between speed, scalability, security, and cost. 

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Soft Skills 

• Excellent communication and stakeholder management skills. 

• Ability to communicate effectively with both technical and business audiences. 

• Fluent written and spoken English.