AI Engineering Manager

About the role

Blend is seeking an AI Engineering Manager to lead the architecture, development, and delivery of Generative AI and Agentic AI solutions on the Microsoft Azure ecosystem. This technical leadership position focuses on defining strategy, standards, and guiding senior engineers rather than traditional people‑management.

Key responsibilities

• Define the technical strategy for Generative AI and Agentic AI solutions built on Azure.
• Lead architecture and delivery using Azure AI Foundry and Microsoft Fabric.
• Design scalable agentic workflows, including agents, skills, tools, orchestration, memory, and enterprise integration.
• Architect Retrieval‑Augmented Generation (RAG) and knowledge‑graph solutions, covering retrieval, chunking, embeddings, vector search, grounding, and graph construction.
• Establish standards for building, testing, evaluating, and deploying AI applications.
• Define and oversee AI evaluation frameworks measuring quality, accuracy, relevance, safety, and performance.
• Review technical designs, provide hands‑on guidance, and transition prototypes to production‑grade systems.
• Mentor and grow a team of Senior AI Engineers and technical specialists.
• Collaborate with product, data, engineering, and business leaders to prioritize high‑value AI opportunities.
• Manage technical risks, dependencies, scalability, and delivery across multiple initiatives.

Required profile

• Proven experience leading highly technical engineering teams in AI/ML.
• Deep expertise in Azure AI services, especially Azure AI Foundry and Microsoft Fabric.
• Strong background in Generative AI, RAG, knowledge graphs, and agentic AI architectures.
• Ability to define technical direction, set standards, and mentor senior engineers.
• Excellent communication skills to convey complex technical concepts to both technical and non‑technical stakeholders.

Required skills

• Azure AI Foundry
• Microsoft Fabric
• Generative AI and Agentic AI workflows
• Retrieval‑Augmented Generation (RAG)
• Knowledge graphs
• Vector search and embeddings
• AI evaluation frameworks
• Enterprise system integration