Microsoft Foundry SME

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. 

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• 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. 

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