Job Description

We are looking for an AI Engineering Manager to lead the architecture, development, and delivery of Generative AI and Agentic AI solutions across the Microsoft Azure ecosystem.

This is a technical leadership role, not a traditional people-management position. The adecuado candidate will have deep expertise in Azure AI Foundry, Microsoft Fabric, RAG, knowledge graphs, agentic workflows, AI agents, skills, and evaluation frameworks, combined with experience leading highly technical engineering teams.

The AI Engineering Manager will define technical direction, establish engineering standards, mentor senior engineers, and remain close enough to the technology to make and challenge key architecture decisions.

Key Responsibilities

- Define the technical strategy for Generative AI and Agentic AI solutions built primarily on Microsoft Azure.
- Lead the architecture and delivery of solutions using Azure AI Foundry and Microsoft Fabric.
- Define scalable patterns for agentic workflows, including agents, skills, tools, orchestration, memory, and enterprise system integration.
- Lead the design of RAG and knowledge-based AI architectures, including retrieval, chunking, embeddings, vector search, grounding, and knowledge graphs.
- Establish technical standards for building, testing,



evaluating, and deploying AI applications.
- Define and oversee AI evaluation (evals) strategies to measure quality, accuracy, relevance, reliability, safety, and performance.
- Guide teams in selecting appropriate models, retrieval strategies, agent architectures, and AI technologies.
- Review technical designs and architecture decisions and provide hands-on technical guidance when needed.
- Lead the transition of AI solutions from experimentation and proof-of-concept stages into reliable production systems.
- Build and mentor a team of Senior AI Engineers and other technical specialists.
- Partner with Product, Data, Engineering, and business leadership to identify and prioritize high-value AI opportunities.
- Establish reusable frameworks, components, and engineering practices across AI initiatives.
- Manage technical risks, dependencies, scalability considerations, and delivery across multiple AI initiatives.
- Communicate complex AI architecture and technical tradeoffs clearly to both technical and non-technical stakeholders.
- Stay current with developments in agentic AI, LLMs, Azure AI, AI evaluation, knowledge graphs, and enterprise AI architectures.

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