Job Summary:

We are looking for an experienced AI Architect to design and implement scalable Generative AI, Agentic AI, and enterprise AI solutions.

Key Responsibilities:

- Design end-to-end AI/GenAI architectures for enterprise applications.

- Build Agentic AI and multi-agent workflows using frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, or CrewAI.

- Develop RAG-based solutions using Azure AI Search and vector databases.

- Work with Azure OpenAI, Microsoft Copilot, Copilot Studio, and Azure AI Foundry.

- Design prompt engineering, embeddings, chunking, retrieval, evaluation, and LLM orchestration strategies.

- Develop AI solutions using Python, APIs, Azure Functions, Service Bus, and microservices.

- Implement enterprise security using Azure Key Vault, Managed Identity, RBAC, private endpoints, and networking.

- Design AI/ML deployment pipelines, monitoring, telemetry, and governance.

- Collaborate with product managers, developers, data engineers, and business stakeholders to convert requirements into AI solutions.

- Establish best practices for AI security, responsible AI, scalability, performance, and cost optimization.

- Mentor engineering teams and provide technical leadership across AI initiatives.

Required Skills:

- 10+ years of software/technology experience with 3 - 5+ years in AI/ML/GenAI.

- Strong Python programming skills.

- Hands-on experience with: Generative AI / LLMs, RAG architecture, Vector databases, Prompt engineering, Agentic AI / Multi-Agent systems, LLM evaluation and observability.

- Strong Azure experience: Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure Functions, Azure Service Bus, Azure Key Vault, Azure Storage, Azure Kubernetes Service (AKS).

- Experience with Microsoft Copilot / Copilot Studio is preferred.

- Knowledge of C#, .NET / ASP.NET Core and REST APIs is an advantage.

- Experience with Docker, Kubernetes, CI/CD, Git, and cloud-native architecture.

- Strong understanding of enterprise architecture, security, scalability, and integration patterns.

Preferred Qualifications:

- Microsoft Azure AI Engineer / Azure Solutions Architect certification.

- Experience with MCP, A2A protocols, tool calling, function calling, and AI agents.

- Experience integrating AI with Microsoft 365 / enterprise applications.

- Experience with MLOps/LLMOps and AI governance.

- Experience in designing production-grade AI platforms for large enterprises.