Technical Architect-Azure AI

JOB DESCRIPTION

We are seeking a hands-on, innovative Azure AI Architect to join our advanced AI engineering team. In this role, you will work at the forefront of applied AI—extending Microsoft Copilots, building Retrieval-Augmented Generation (RAG) pipelines, and crafting intelligent AI agents for real-world business applications. You will leverage Azure AI Studio, Semantic Kernel, Azure AI Search, and other services across the Microsoft AI stack.

Key Responsibilities

Pre-Sales & Client Engagement
• Act as the AI subject matter expert in client conversations, workshops, and solution envisioning sessions.
• Support proposal development, architecture documentation, and effort estimation for AI projects.
• Translate complex business needs into high-level and detailed technical architecture diagrams and blueprints.

Design Azure AI Solutions
• Define architectural patterns for integrating LLMs, generative AI, RAG pipelines, vector databases (e.g., Azure AI Search, FAISS), and orchestration frameworks (e.g., Semantic Kernel, LangChain).
• Support team in building Azure Cost estimates for various types of solution archetypes, provide guidance on selecting the most cost-effective Azure AI service configurations (e.g., model size, region, compute SKUs).
• Ensure scalability, security, governance, and compliance are foundational in all designs.

Copilot Extension & Customization
• Provide subject matter expertise in using Azure AI Studio to extend and fine-tune Microsoft 365 Copilots and custom business Copilots.
• Integrate enterprise data and logic to create domain-specific copilots aligned with customer workflows.
• Apply prompt engineering and grounding strategies to enhance context awareness and action-taking capabilities.

Advanced AI Pipeline Development
• Build retrieval-augmented generation (RAG) pipelines and advanced RAG pipelines (such as GraphRAG) using Azure AI Search, Cognitive Search, and Semantic Kernel.
• Design chunking, embedding, ranking, and post-processing strategies to optimize search relevance and model responses.
• Connect unstructured and structured content (e.g., PDFs, Word, Excel, D365 data) to GenAI models for personalized search and Q&A.
• Leverage Azure Custom Skills to enrich data across the pipeline to build enterprise-grade scaled AI solutions

AI Agent Design and Orchestration
• Design and deploy multi-step AI agents that reason, retrieve, and take action based on dynamic workflows.
• Develop solution archetypes (e.g., knowledge assistants, workflow copilots, policy advisors) powered by Semantic Kernel or Azure OpenAI Function Calling.
• Implement agent memory, state management, and tool integration (APIs, plugins, functions).

AI Lifecycle Engineering
• Automate deployment using Azure DevOps, CI/CD, and containerization where needed.
• Monitor model outputs for relevance, accuracy, and alignment to responsible AI principles.
• Collaborate with data engineers, solution architects, and UX designers for full-stack deployment.