Azure Agentic AI Architect

Azure Agentic AI Architect | Location: Bay Area Preferred / Remote

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.