AI Engineer

Must-Have Qualifications

Core requirements:

• Excellent English communication — able to clearly understand spoken and written English, and express ideas and collaborate in confident, fluent, and precise English.
• Growth mindset and drive — eager to learn and improve, proactive and self-motivated, self-critical and open to feedback, while inclusive and self-assured.
• Alignment and reliable execution — stays aligned with company and team strategy and roadmap, and dependably executes and delivers on commitments.

Role-specific:

• A solid software engineering foundation — system design, data structures, concurrency, error handling, and building services that are reliable in production. You treat AI systems with the same engineering rigor as any other software you ship.
• Strong programming skills, ideally in Python and/or TypeScript/C#. You're comfortable working close to the metal — for example, implementing a tool-calling loop yourself rather than depending on a framework to handle it for you.
• Practical experience building AI applications, agents, assistants, or LLM-integrated workflows, and the ability to clearly explain what you built, the decisions you made, and what you'd do differently next time.
• A real grasp of the fundamentals beneath the frameworks: how APIs, backend services, and orchestration fit together; how prompts and context are structured and managed within a model's limits; and how tool/function calling works.
• The ability to debug and evaluate AI systems — to figure out why a model's output is wrong, and to measure whether a change genuinely improved things rather than relying on gut feel.
• Awareness of cost, latency, and reliability trade-offs, and the judgment to choose the simplest approach that solves the problem well.
• Good instincts for matching the technique to the problem — retrieval, fine-tuning, prompt engineering, or plain function calling — based on what the work actually needs.

Experience-to-Have

• Experience with the Microsoft technology stack (e.g. .NET/C#, Azure, Azure OpenAI, Microsoft Fabric).
• Data engineering skills are a big plus — dimensional modeling (star and snowflake schemas), ETL pipelines, and working with big data.
• Familiarity with enterprise data platforms and semantic layers.
• Experience designing AI systems that are grounded, explainable, permission-aware, and aligned with governed data sources.
• Standard engineering practice: Git, CI/CD, testing, logging, monitoring, and deployment.