Microsoft Foundry SME

Job description

Role Summary Avanade India’s Artificial Intelligence practice is looking for a Microsoft Foundry SME to be a deep technical specialist on generative AI and agentic AI projects built on Microsoft Foundry. This is a hands-on, individual-contributor role for someone who wants to own the technical build — designing and implementing AI components, solving complex implementation challenges, and providing specialist guidance to the wider delivery team. Key Responsibilities Design and build components of Microsoft Foundry solutions: model catalog integration, prompt flow/orchestration, agent design, and RAG pipelines (Azure AI Search, vector indexing, grounding data). Implement multi-agent orchestration patterns using frameworks such as Semantic Kernel, AutoGen, or LangChain/LangGraph. Act as the technical specialist/go-to resource on Microsoft Foundry within project teams, resolving complex implementation issues and edge cases. Support the Microsoft Foundry Lead/Architect in solution design, technical estimation, and implementation planning. Write and review production-quality code; ensure adherence to engineering best practices, testing, and CI/CD standards. Apply Responsible AI, content safety, and governance guardrails during build and deployment. Support post-go-live stabilization and production issue troubleshooting for deployed AI solutions. Guide junior engineers on Microsoft Foundry implementation patterns and best practices, without formal team-lead accountability. Contribute to reusable code assets, accelerators, and technical documentation for the AI Engineering job family. Stay current with the Microsoft Foundry roadmap and broader generative AI ecosystem, evaluating new capabilities for project use. . 6–9 years in AI/ML or software engineering roles, with strong recent hands-on experience building solutions on Microsoft Foundry (or Azure AI Foundry/Azure AI Studio). Hands-on experience with Azure OpenAI Service, Microsoft Foundry model catalog, prompt engineering/prompt flow, and agent design. Practical experience building RAG solutions — vector databases/embeddings, Azure AI Search, and enterprise data grounding patterns. Working experience with agentic AI implementation — multi-agent orchestration, tool/function calling, and frameworks such as Semantic Kernel, AutoGen, or LangChain/LangGraph. Familiarity with the broader Azure data and AI ecosystem: Azure Machine Learning, Azure Cognitive Services, and data pipelines (Azure Data Factory/Synapse or Databricks) feeding AI solutions. Working knowledge of MLOps/LLMOps practices — model deployment, versioning, monitoring, and CI/CD for AI solutions. Awareness of Responsible AI principles and content safety/filtering practices. Strong programming skills in Python; familiarity with REST APIs and integration patterns for enterprise applications. Comfortable working directly with technical leads and client technical teams during delivery. Preferred / Good to Have Microsoft certifications: Azure AI Engineer Associate (AI-102) or other Generative AI-related Microsoft credentials. Experience with Copilot Studio or Microsoft 365 Copilot extensibility. Exposure to CI/CD and DevOps practices for AI/data platforms (Azure DevOps, GitHub Actions). 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. .