Azure Agentic AI Architect

Hi ,

This is Vamshi ,from Software Technology We have a job opening with our client for position Azure Agentic AI Architect If you are available and looking for any new opportunities, please send me your updated resume for below position ASAP.

Job Title: Azure Agentic AI Architect

Location: Dallas or Houston (Remote)

Duration: Full Time

Job Description

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 12-15 years of experience in AI/ML engineering and architecture, 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.

Thanks,

Vamshi Thangadpalli

Technical Recruiter

Email: | Web:

100 Overlook Center, Suite 200

Princeton, NJ 08540.