[Hiring] Azure Agentic AI Architect @Software Technology

Role 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.

Qualifications

• 12-15 years of experience in AI/ML engineering and architecture
• 2 years of experience in Generative/Agentic AI
• Strong leadership capabilities
• Hands-on experience designing and deploying production-grade AI solutions on Azure

Requirements

• 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
• 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
• 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
• 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
• 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

Company Description