Ai Architect

Key Responsibilities

AI Platform Strategy & Technology Roadmap

• Monitor and evaluate emerging AI technologies across Azure AI Foundry, Azure OpenAI, Microsoft Copilot, and the broader Generative AI ecosystem.
• Assess new AI capabilities and recommend adoption based on business value, technical feasibility, security, and scalability.
• Collaborate with Product Owners and Solution Architects to define and evolve the enterprise AI platform roadmap.
• Develop Proof of Concepts (PoCs), prototypes, and feasibility studies to validate new AI services before enterprise adoption.
• Provide technical leadership on AI platform strategy and architecture decisions.

AI Platform Architecture

• Design scalable, secure, and resilient AI platform capabilities using Azure AI Foundry.
• Architect enterprise Retrieval-Augmented Generation (RAG) solutions integrating Azure AI Search, vector databases, and enterprise knowledge repositories.
• Design AI Agent architectures using Semantic Kernel, Azure AI Agent Service, and modern orchestration frameworks.
• Build multi-model and multimodal AI solutions leveraging Azure OpenAI, Azure AI Services, Document Intelligence, Vision, and Speech services.
• Design enterprise integration patterns connecting AI services with business applications, APIs, and enterprise data sources.
• Define reusable AI reference architectures, design standards, and implementation guidelines.

Cloud & Platform Engineering

• Design cloud-native architectures using Azure Kubernetes Service (AKS), Azure App Services, Azure Functions, Azure API Management, and Azure Storage.
• Develop Infrastructure as Code (IaC) using Terraform, Bicep, or ARM templates.
• Design secure networking using Virtual Networks, Private Endpoints, Managed Identity, Key Vault, and API Management.
• Define deployment architectures for highly available, scalable AI platforms.
• Optimize platform performance, reliability, resiliency, and cost.

AI Engineering & Development

• Develop enterprise AI applications using Azure AI Foundry SDK, Azure OpenAI Service, Semantic Kernel, LangChain, and Python/.NET.
• Design prompt engineering strategies and evaluate Large Language Models (LLMs).
• Build document ingestion, chunking, embedding generation, and semantic retrieval pipelines.
• Implement AI Agents and workflow orchestration for enterprise automation.
• Create reusable AI accelerators, starter templates, and deployment frameworks.

DevSecOps & Platform Automation

• Implement CI/CD pipelines using GitHub Actions and Azure DevOps.
• Automate testing, deployment, and environment provisioning.
• Integrate security scanning including SAST, DAST, dependency scanning, and policy validation.
• Utilize Azure Developer CLI (azd) to standardize deployment processes.
• Promote Infrastructure as Code and GitOps best practices.

Security & Responsible AI

• Design secure AI solutions following Microsoft's Security Development Lifecycle (SDL).
• Conduct threat modeling and architecture security reviews for AI solutions.
• Implement protections against prompt injection, jailbreak attacks, and data leakage.
• Configure Azure AI Content Safety and implement enterprise content moderation policies.
• Apply OWASP LLM Top 10 recommendations throughout the AI solution lifecycle.
• Ensure compliance with Responsible AI principles, including fairness, transparency, accountability, privacy, reliability, and human oversight.
• Support compliance with GDPR, the EU AI Act, and organizational AI governance requirements through model documentation, data lineage, audit logging, and risk assessments.

Architecture Governance & Leadership

• Define enterprise AI platform standards and architecture governance.
• Conduct architecture reviews and design assurance for AI initiatives.
• Mentor architects and engineering teams on Azure AI technologies and best practices.
• Collaborate with security, infrastructure, and business teams to ensure successful AI adoption.