AI Architect with Azure
Summary
Architects enterprise AI and Generative AI solutions on Microsoft Azure at TCS: designs RAG and agentic/multi-agent systems using Azure OpenAI, Azure AI Search, AI Foundry and Azure ML, builds Python/FastAPI integration layers, and defines security, governance, observability and CI/CD for production AI workloads.
Role: AI Architect Azure / Generative AI
Experience: 1015 Years
Locations: Chennai / Delhi / Pune / Mumbai / Kochi
Role Overview
We are looking for an experienced AI Architect to design and deliver enterprise-scale AI and Generative AI solutions on Microsoft Azure. The role requires strong hands-on architecture experience across Azure OpenAI, Agentic AI, RAG, Azure AI services, Python-based integrations, security, governance, and production deployment.
Key Responsibilities
- Design end-to-end architecture for enterprise AI, Generative AI and Agentic AI solutions on Azure.
- Architect solutions using Azure OpenAI, Azure AI Search, Azure AI Foundry and Azure Machine Learning.
- Design and implement RAG architectures, vector/semantic search and enterprise knowledge retrieval.
- Design agentic and multi-agent workflows, including tool/function calling and enterprise API integration.
- Build scalable AI integration layers using Python, FastAPI and REST APIs.
- Define architecture patterns for model/LLM orchestration, prompt management, response validation and evaluation.
- Establish enterprise AI controls covering RBAC, PII protection, prompt security, content filtering, auditability and human-in-the-loop workflows.
- Architect cloud-native deployment and CI/CD approaches using Azure services, containers and DevOps practices.
- Define observability and monitoring standards for production AI workloads.
- Collaborate with business, engineering, security and governance teams to translate business requirements into scalable AI solutions.
Must-Have Skills
- Strong experience in AI/ML solution architecture and Azure cloud architecture
- Azure OpenAI
- Generative AI / LLM architecture
- RAG and Azure AI Search
- Agentic AI / AI Agents
- Python and FastAPI/API integration
- Azure AI services such as Azure AI Foundry and/or Azure Machine Learning
- Enterprise integration and cloud-native solution architecture
- AI security, governance and responsible AI controls
- Docker, CI/CD and production deployment concepts
Good to Have
- Multi-Agent Systems and tool/function calling
- MCP or similar agent-to-tool integration patterns
- Vector Search, embeddings and semantic retrieval
- MLflow and model lifecycle management
- Azure Container Apps / Azure Container Registry
- GitHub Actions / Jenkins
- OpenTelemetry / Azure Monitor / Dynatrace
- PyTorch / TensorFlow / Transformers / NLP