AI Architect

Responsibilities

• Design and implement Azure-based Agentic AI solutions.
• Architect enterprise AI platforms using Azure AI Foundry, Agent Services, APIs, and modern data pipelines.
• Develop multi-agent workflows including reasoning, tool/function calling, memory management, and agent-to-agent communication.
• Integrate Generative AI, NLP, and ML models into enterprise applications.
• Develop RAG-based solutions using Azure AI Search.
• Build AI solutions such as copilots, virtual assistants, intelligent automation, recommendation systems, and analytics platforms.
• Design scalable AI architecture across interaction, orchestration, integration, model, and data layers.
• Evaluate AI/ML models based on performance, scalability, cost, and use-case requirements.
• Implement security, governance, and Responsible AI practices using Azure capabilities.
• Implement monitoring, evaluation, observability, and LLMOps practices.
• Work with product, engineering, and business teams to translate requirements into scalable AI solutions.
• Create architecture diagrams, technical documentation, and deployment strategies.
• Lead POCs and innovation initiatives for AI use cases.

Qualifications

• Strong experience in designing and implementing enterprise AI solutions using Azure technologies.
• Experience with Azure ML, Azure OpenAI, Azure AI Services, and Cognitive Services.
• Experience with Azure AI Search, Agentic AI, and multi-agent frameworks like LangChain, LangGraph, LangSmith, or Semantic Kernel.
• Experience with Generative AI, LLMs, RAG, and prompt engineering in Python and REST APIs.
• Experience with vector databases, embeddings, and semantic search.
• Experience with Databricks, Synapse, and Data Factory, AKS, Docker, and Kubernetes.
• Experience with GitHub / Azure DevOps for MLOps / LLMOps.