We are seeking a seasoned Azure AI Architect with deep expertise in Azure Generative AI, LLM-based solutions, and agentic AI systems. The ideal candidate brings 12+ years of experience in AI/ML engineering and architecture, 1-2 years of experience on Generative/ Agentic AI, strong leadership capabilities, and hands-on experience designing and deploying production-grade AI solutions on Azure. This role combines strategic architecture ownership with hands-on technical leadership.
Roles & Responsibilities
AI Architecture & Technical Leadership
• Define and own the Azure AI architecture roadmap for GenAI, search, recommendation, and agentic AI solutions.
• Design scalable, secure, and cost-optimized AI architectures using Azure-native services.
• Provide technical leadership and mentorship to AI/ML engineers and data scientists.
• Partner with business and engineering leaders to align AI initiatives with enterprise objectives.
Azure Generative AI & Agentic AI
• Architect and deliver Generative AI solutions using Azure OpenAI, Azure AI Foundry, and Azure AI Services.
• Design and implement agentic AI workflows using LangChain, LangGraph, and tool/function calling patterns.
• Build autonomous and semi-autonomous agents capable of reasoning, planning, and orchestration.
• Establish best practices for prompt engineering, evaluation, monitoring, and governance.
LLMs, APIs & Application Integration
• Integrate LLMs into enterprise applications using Azure Functions, REST APIs, and event-driven architectures.
• Implement secure, scalable API layers for AI services with authentication, throttling, and observability.
• Drive adoption of LLMOps practices including prompt versioning, model monitoring, and feedback loops.
Search, Retrieval & Data Platforms
• Architect RAG-based solutions using Azure AI Search (Cognitive Search), Cosmos DB, and vector stores.
• Design semantic search, ranking, and recommendation pipelines for high-quality user experiences.
• Implement real-time and batch feedback loops to continuously improve relevance and accuracy.
MLOps, Security & Responsible AI
• Define end-to-end ML/GenAI lifecycle architecture: ingestion, orchestration, deployment, monitoring.
• Leverage Azure-native DevOps and CI/CD pipelines for AI workloads.
• Ensure compliance with Responsible AI, data governance, security, and enterprise standards.
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