Virtusa
Generative Architect
Skills
2 months ago
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Important information
Contract type:
Permanent
Location:
Chennai, Tamil Nadu, India
Work mode:
Onsite
Published on:
31 August 2026
What they need
Agentic AI & Generative AI (Azure)
Experience: 10-15 years
We are looking for a senior engineer with 7—10 years of overall software/ML engineering experience, including at least 3 years building Agentic AI and Generative AI solutions on Microsoft Azure, to design, build, and deploy production-grade agentic and GenAI systems across the full AI stack.
Responsibilities
- Architect and build GenAI solutions using Microsoft Foundry and Azure OpenAI, selecting models via the Foundry model catalog (OpenAI, Llama, Mistral, MAI)
- Design and own Retrieval-Augmented Generation (RAG) pipelines using Azure OpenAI Embeddings, Azure AI Search, and Foundry IQ
- Lead development of production AI agents using the Microsoft Agent Framework on Foundry Agent Service, with tool-calling via the Model Context Protocol (MCP) and multi-agent coordination via the Agent2Agent (A2A) protocol
- Define and enforce Azure AI Content Safety guardrails and lead Foundry evaluations before production release
- Own MLOps automation with Azure ML / MLflow and drive fine-tuning of models on proprietary data
- Mentor engineers and guide architecture decisions for agentic AI adoption across teams
Required Skills
- 10-15 years overall software/ML engineering experience, including at least 3 years hands-on with Agentic AI and Generative AI specifically
- Deep hands-on experience with Microsoft Foundry and/or Azure OpenAI in a production environment
- Strong grasp of embeddings, vector search, and Retrieval-Augmented Generation (RAG) architecture
- Practical experience with the Microsoft Agent Framework, MCP tool-calling, and A2A multi-agent patterns
- Strong Python/.NET and Azure fundamentals (Blob Storage, Entra ID, Container Apps)
Preferred
- Microsoft Certified: Azure AI Fundamentals (AI-901) or Azure AI Apps and Agents Developer Associate (AI-103)
- Experience with MLOps, CI/CD, and production model monitoring at scale
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