Azure AI Foundry Architect

Key Responsibilities

• Lead presales solutioning for Azure AI Foundry opportunities — architecture proposals, effort estimation, solution demos, and client workshops — partnering with account and practice leadership on pursuits.
• Design end-to-end generative AI and agentic AI solution architectures on Azure AI Foundry, including model catalog selection, Azure OpenAI Service integration, prompt flow/orchestration, and agent design.
• Architect Retrieval-Augmented Generation (RAG) solutions — grounding data pipelines, vector search/indexing (Azure AI Search), and integration with enterprise data sources.
• Define AI platform strategy, target-state architecture, and adoption roadmaps in collaboration with client stakeholders.
• Step into delivery as needed to provide technical leadership on active engagements: solution design, architecture governance, design reviews, performance/cost optimization, and production readiness.
• Establish architecture standards for Responsible AI, content safety, model governance, security, and observability across AI solutions.
• Guide multi-agent orchestration design using frameworks such as Semantic Kernel, AutoGen, or LangChain/LangGraph where applicable.
• Act as a trusted technical advisor to client stakeholders, translating business requirements into scalable, well-governed AI architectures.
• Mentor AI/data engineers and architects on project teams; contribute to reusable accelerators and IP for the practice.
• Stay current with the Azure AI Foundry roadmap and broader generative AI ecosystem, bringing new capabilities into client proposals and solutions.
• 12–15 years in AI/ML, data engineering, or solution architecture roles, with recent hands-on experience architecting solutions on Azure AI Foundry (or Azure AI Studio).
• Hands-on experience with Azure OpenAI Service, Azure AI Foundry model catalog, prompt engineering/prompt flow, and fine-tuning approaches.
• Strong experience designing RAG architectures — vector databases/embeddings, Azure AI Search, and enterprise data grounding patterns.
• Experience with agentic AI design patterns — multi-agent orchestration, tool/function calling, and frameworks such as Semantic Kernel, AutoGen, or LangChain/LangGraph.
• Solid grounding in the broader Azure data and AI ecosystem: Azure Machine Learning, Azure Cognitive Services, Azure Data Factory/Synapse or Databricks for data pipelines feeding AI solutions.
• Working knowledge of MLOps/LLMOps practices — model deployment, versioning, monitoring, and CI/CD for AI solutions.
• Understanding of Responsible AI principles, content safety/filtering, and AI governance frameworks in enterprise environments.
• Strong programming skills in Python; familiarity with REST APIs and integration patterns for enterprise applications.
• Demonstrated experience in presales solutioning — proposals, estimation, and client-facing workshops — alongside hands-on delivery capability.
• Experience mentoring teams and owning technical delivery quality on medium-to-large engagements.
• Preferred / Good to Have
• Microsoft certifications: Azure AI Engineer Associate (AI-102), Azure Solutions Architect Expert, or Generative AI-related Microsoft credentials.
• Experience with Copilot Studio or Microsoft 365 Copilot extensibility.
• Exposure to broader cloud AI platforms (AWS Bedrock, Google Vertex AI) for comparative positioning.
• Experience working across multiple industry domains (manufacturing, BFSI, consumer goods, energy, healthcare, or others).
• Experience working in a global delivery / SI environment with distributed teams across geographies.
• Prior experience contributing to reusable accelerators, frameworks, or practice-building initiatives.