Job Overview
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Job Description
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 at Microsoft
- 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.
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