Artificial Intelligence Architect

Role & responsibilities

• Lead the architecture and design of enterprise AI and Generative AI solutions.
• Design scalable solutions using Microsoft Foundry / Azure AI Foundry, Azure OpenAI, and Azure AI services.
• Architect LLM, RAG, AI Agent, embeddings, vector search, and enterprise knowledge retrieval solutions.
• Translate customer business requirements into practical and production-ready AI architectures.
• Conduct customer workshops, technical discovery sessions, and architecture discussions.
• Review solution architectures across AI, applications, data, integration, infrastructure, and security.
• Define reference architectures, technical standards, reusable patterns, and implementation approaches.
• Evaluate models and architecture options based on accuracy, scalability, latency, security, reliability, and cost.
• Design secure integrations between AI applications, APIs, enterprise systems, and data platforms.
• Ensure solutions address performance, resilience, observability, monitoring, privacy, and access-control requirements.
• Review technical designs and implementation approaches created by engineering teams.
• Provide technical direction to AI Engineers, Data Engineers, Cloud Architects, and development teams.
• Resolve complex architecture decisions, technical risks, and implementation blockers.
• Support pre-sales and solutioning activities for AI and Generative AI opportunities.
• Contribute to solution scope, technical approach, assumptions, dependencies, and effort estimates.
• Guide AI proof-of-concepts and prototypes toward secure and scalable production deployments.
• Develop reusable AI accelerators, reference implementations, and architecture assets.
• Present architecture recommendations, trade-offs, and technical decisions to senior customer stakeholders.

Preferred candidate profile

The ideal candidate should have:

• 5+ years of relevant experience across AI/ML, software engineering, cloud architecture, solution architecture, or data engineering.
• Strong recent hands-on experience designing and delivering Generative AI solutions.
• Practical experience with Microsoft Foundry / Azure AI Foundry and Azure OpenAI.
• Strong understanding of Large Language Model (LLM) application architecture.
• Hands-on experience designing Retrieval-Augmented Generation (RAG) solutions.
• Experience with AI Agents, Agentic AI, tool/function calling, and agent-based workflows.
• Experience with embeddings, vector search, vector databases, and enterprise knowledge retrieval.
• Strong experience with cloud-native application architecture on Microsoft Azure.
• Experience designing APIs, integrations, microservices, and distributed applications.
• Knowledge of enterprise security including identity, networking, authentication, authorization, data privacy, and access control.
• Ability to design AI solutions for production with consideration for scalability, availability, reliability, monitoring, latency, security, and cost.
• Ability to review technical architecture and implementation approaches created by engineering teams.
• Strong customer-facing communication, consulting, presentation, and stakeholder-management skills.
• Ability to communicate complex technical concepts to both engineers and senior business/technology stakeholders.