Join our Purview AI Classification team, where we engineer sophisticated AI capabilities for enterprise-level sensitive information identification. Our focus is on advanced Named Entity Recognition (NER) and robust AI evaluation. We specialize in developing and refining ML/NLP models for intricate entity recognition challenges, alongside creating evaluation methodologies to precisely gauge accuracy, precision, recall, and model advancements across diverse datasets and real-world applications. As an Applied Scientist, you will spearhead experimentation, conduct detailed dataset and error analysis, drive model evaluation, and implement iterative improvements for NER and classification quality. Collaboration with engineering and product teams will be key to translating scientific innovations into scalable solutions within Microsoft Purview.

Key responsibilities include designing, developing, and deploying end-to-end AI/ML systems, encompassing data ingestion, feature engineering, model training, evaluation, and production integration. You will build and optimize Generative AI and LLM-based systems, focusing on agentic workflows, prompt engineering, RAG, and fine-tuning. Writing production-grade code in Python and C# is essential, with a strong emphasis on scalability, performance, security, testability, and maintainability.

Collaborate with cross-functional teams (engineering, product management, applied science) to transform business requirements into robust technical solutions. You will be responsible for shipping and operating large-scale AI services in the cloud, ensuring reliability, low latency, high throughput, accuracy, and cost efficiency.

Define and execute comprehensive model evaluation strategies, including offline experimentation, online monitoring, drift detection, bias analysis, and feedback loops. Implement MLOps practices for model CI/CD, versioning, rollout strategies, observability, and live-site monitoring. Apply Responsible AI principles—privacy, security, explainability, fairness, and compliance—throughout the development and deployment lifecycle. Stay abreast of the latest advancements in GenAI, LLM frameworks, and ML infrastructure, assessing their relevance for enterprise security scenarios.

We are seeking candidates with a Bachelor’s degree in Computer Science, Data Science, Engineering, or a closely related technical field. A minimum of 5 years of overall experience is required, including substantial hands-on model development and experience writing production-quality code.

A solid understanding of Machine Learning fundamentals, model evaluation techniques, experimental design, and performance trade-offs is crucial. Prior experience in building or operationalizing LLM / Generative AI systems, including familiarity with RAG, prompt engineering, or agent-based architectures, is highly desirable.

Successful candidates will demonstrate a strong ability to collaborate effectively across diverse disciplines and operate with autonomy at a senior individual contributor level.