Data Analytics Manager (AI Engineer - Generative & Agentic AI) at Microsoft | Apply at Microsoft

About the role

AI Engineer - Generative & Agentic AI

We are seeking an AI Engineer to lead the design and deployment of Agentic AI solutions within the Microsoft Azure AI ecosystem. Your main responsibility will be to construct autonomous multi-agent systems capable of reasoning, acting, and collaborating to tackle complex challenges. While Generative AI (such as LLMs like GPT) will support your efforts, it is not the primary focus. You will spearhead AI-powered automation by integrating Azure AI tools with enterprise systems and workflows. Collaboration with business, data engineering, architecture, and DevOps teams will be crucial for delivering intelligent, scalable, and secure AI solutions.

Responsibilities

Agentic AI Development: Design and deploy autonomous agents using Azure AI Agent Service and related tools, defining agent logic, goals, and collaboration flows.
End-to-End Ownership: Lead the entire lifecycle of multi-agent systems, from design and deployment to scaling.
Cloud Integration: Utilize Azure services such as AI Studio, OpenAI, Azure ML, Functions, and Kubernetes for deployment and orchestration.
Generative AI Use: Integrate LLMs (e.g., GPT) for tasks like summarization, Q&A, retrieval-augmented generation, and natural language processing.
Enterprise Automation: Enhance agent capabilities through Power Platform tools including Power Automate, Power Apps, and Dataverse.
Innovation & Reusability: Contribute to internal R&D, develop reusable assets, and disseminate knowledge across teams.

Qualifications

Required Qualification:

3–6 years in software engineering or AI/ML development, with 1–2 years specifically in agent-based AI.
Strong proficiency in Python for AI workflows, APIs, and automation.
Demonstrated experience deploying AI solutions using Microsoft’s Azure AI stack (AI Studio, Azure ML, OpenAI, Agent Service).

Preferred Qualification:

Understanding of agent orchestration, stateful logic, decision systems, and tool-using agents.
Familiarity with prompt engineering, RAG, or LLM fine-tuning.
Knowledge of cloud-native architecture, APIs, event-driven systems, and secure deployment practices.
Programming Languages: Python (core scripting for pipelines, agent logic, orchestration), JavaScript/TypeScript (for AI-embedded apps, web integrations), Power Fx, DAX, M (for Power Platform and Power BI customizations), C#/.NET (preferred for Azure Functions and Bot Framework).
APIs & Data Standards: Experience with JSON, YAML, REST, GraphQL, and Microsoft Graph.
Visualization (Nice to have): Experience with custom Power BI visuals using D3.js or Chart.js.

Key skills/competency

Agentic AI Development
Azure AI Ecosystem
Multi-Agent Systems
Python Development
Cloud Integration
Generative AI
Enterprise Automation
Solution Design
AI/ML Development
Software Engineering

How to get hired

Tailor your resume: Highlight Python, Azure AI, and agent-based AI experience.
Showcase Azure expertise: Emphasize projects using AI Studio, OpenAI, and Agent Service.
Demonstrate automation skills: Detail experience with enterprise system integration.
Prepare for technical interviews: Brush up on agent logic, orchestration, and cloud architecture.
Understand Microsoft's mission: Align your experience with their focus on AI innovation.

Frequently asked questions

What are the primary responsibilities for an AI Engineer at Microsoft focusing on Agentic AI?

As an AI Engineer at Microsoft specializing in Agentic AI, your primary responsibilities include designing and deploying autonomous multi-agent systems using Azure AI Agent Service, defining agent logic and collaboration flows, integrating AI solutions with enterprise systems, and contributing to internal R&D. You'll leverage various Azure AI services and Power Platform tools to drive AI-powered automation.

What technical skills are most critical for this AI Engineer role at Microsoft?

The most critical technical skills for this AI Engineer role include strong proficiency in Python for AI workflows and automation, hands-on experience with Microsoft's Azure AI stack (AI Studio, Azure ML, OpenAI, Agent Service), and a solid understanding of agent-based AI concepts like agent orchestration and decision systems. Familiarity with cloud-native architecture and APIs is also highly valued.

Does this AI Engineer position heavily involve Generative AI, or is Agentic AI the main focus?

While Generative AI (like LLMs such as GPT) will complement your work, the core focus of this AI Engineer position is on building and deploying Agentic AI solutions and autonomous multi-agent systems. Generative AI will be integrated where needed for specific tasks such as summarization or Q&A.

How can I best highlight my qualifications for the AI Engineer role at Microsoft?

To best highlight your qualifications, tailor your resume to emphasize your direct experience with agent-based AI, your proficiency in Python for AI development, and your practical experience deploying solutions using the Microsoft Azure AI ecosystem. Quantify your achievements whenever possible, especially in areas like enterprise automation and system integration.

What is Microsoft's approach to AI development, and how does this role fit in?

Microsoft is committed to advancing AI responsibly and inclusively. This AI Engineer role is central to that mission, focusing on developing cutting-edge Agentic AI solutions that drive intelligent automation and solve complex business problems through the Azure AI ecosystem, fostering innovation and reusability across the organization.

Data Analytics Manager (AI Engineer - Generative & Agentic AI)

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