Role SummaryEnterprise Architect with 12–15 years of experience in designing production-grade enterprise architectures. The role focuses on GenAI orchestration (RAG, MCP, agent frameworks) and integration with .NET-based systems, working closely with onsite stakeholders and offshore engineering teams. Key Responsibilities · Lead architecture and design of AI Control Pane for enterprise GenAI platform - Define end-to-end architecture integrating UI, backend, data, and AI layers
- Design and implement RAG pipelines, MCP integrations, and agentic workflows
- Architect AI orchestration using LangGraph / LangChain (or similar frameworks)
- Define integration patterns between .NET services, Azure data platforms, and AI systems
- Guide offshore teams in architecture, design, and engineering execution
- Ensure scalability, security, performance, and governance of AI solutions
- Work closely with business stakeholders to translate property domain use cases into architecture
- Review and standardize APIs, adapters, and microservices design patterns
Technology Stack· Backend: .NET / C# ( Core, APIs, microservices) · Cloud: AWS/Microsoft Azure · GenAI: o RAG (Retrieval Augmented Generation) - MCP (Model Context Protocol)
- Agent frameworks (LangGraph, LangChain or similar)
· Integration: REST, gRPC, API Management (APIM), adapters/connectors · Data: Oracle or Azure SQL · Programming: Python (for AI/agent orchestration) Preferred Skills · Experience building AI platforms / control planes / orchestration layers - Exposure to low-code/no-code platforms (Appian or similar)
- Experience with AI governance, LLMOps, and enterprise AI rollout
- Familiarity with map-based or data visualization platforms and with Neo4j / Cosmos DB
Domain Experience· Real Estate / Property Management preferred - BFSI / Lending domain understanding is a plus
- Understanding of property, mortgage, loan, tranche structures preferred
Experience & Profile· 12–15 years in enterprise architecture and solution design - Proven experience in end-to-end system design and large-scale implementations
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