Join Microsoft's Applied AI Research team to pioneer the next generation of AI-powered security and compliance solutions. This role within Microsoft Purview focuses on tackling complex enterprise challenges in data security, governance, and risk management. You will transform innovative ideas into tangible products that benefit millions of users.

As an AI Engineer, you'll work with advanced AI technologies, including large language models and agentic systems. You will collaborate with researchers, engineers, and product leaders to create intelligent experiences that enhance how organizations safeguard their data. This is a unique opportunity to drive technical innovation, influence platform strategy, and shape the future of AI in Microsoft Purview.

Microsoft is dedicated to empowering every person and organization globally. Our team embodies a growth mindset, fosters innovation, and collaborates to achieve shared objectives. We uphold values of respect, integrity, and accountability, cultivating an inclusive environment where everyone can excel.

Design, develop, and maintain robust AI systems that host ML models, APIs, and manage complex workflows. Take ownership of the end-to-end ML/DL lifecycle, from deployment to monitoring and ensuring live-site reliability.

Define success metrics, create evaluation datasets, and implement human-in-the-loop reviews. Troubleshoot AI behavior and performance by analyzing logs, data, and model traces to identify and resolve failure patterns.

Establish and monitor AI quality metrics, integrating them into CI/CD pipelines. Optimize runtime performance, token usage, inference costs, and latency. Implement continuous improvement loops based on online signals and user feedback.

Collaborate with product and domain stakeholders to define use cases, acceptance criteria, and rollout plans for new AI features, ensuring live site responsibility.

A Bachelor's Degree in Computer Science, Data Science, or a related technical field is required, along with a minimum of 5 years of experience in building and maintaining ML/AI systems.

Demonstrate hands-on expertise in MLOps and AIOps. Possess over 4 years of experience deploying at least two large-scale ML/AI-based services or applications on cloud platforms like Azure, AWS, or GCP.

Accumulate 4+ years of technical engineering experience, with proficiency in coding languages such as C# and Python, including PyTorch. Exhibit proficiency in writing production-quality code in modern languages.

Gain 1-2 years of experience with Large Language Models (LLMs) and open-source Generative AI frameworks (e.g., LangChain, LlamaIndex, Transformers, AutoGen). This includes experience with agent-based orchestration, prompt engineering, Retrieval-Augmented Generation (RAG), fine-tuning, and evaluation.