Azure AI Foundry Architect

Job description

Role Summary Avanade India’s Data & AI practice is looking for an Azure AI Foundry Architect to drive presales solutioning and technical architecture for generative AI and agentic AI solutions built on Azure AI Foundry, stepping into delivery leadership as engagements require. This is a client-facing role for someone who enjoys shaping opportunities as much as solving hard architecture problems — from client workshops and proposal-stage solutioning through to guiding implementation teams when needed. 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. .