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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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Skills & Qualifications
- 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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