As the PM for LLM Infrastructure, youll shape the capabilities that turn unstructured data into structured, explainable, and actionable insights. Youll work closely with ML engineers, data scientists, and product leaders to design scalable tools and frameworks that support use cases across personalization, matching, search understanding, trust and safety, and more.
Your mission will be to build reliable and extensible LLM-based systems that serve as core building blocks for future-facing product innovation.
What am I going to do?
Define the product vision and roadmap for LLM infrastructure, aligning with long-term company strategy and cross-team needs.
Lead the development of scalable systems that extract meaning.
Structure from conversational and behavioral data.
Drive adoption of LLM capabilities across by building unified APIs, shared frameworks, and streamlined integration pipelines.
Partner with engineering, data science, and research teams to deliver high-quality infrastructure that powers AI-native product experiences.
Collaborate on system design and architecture decisions to ensure alignment between technical solutions and product scope.
Ensure reliability, scalability, explainability, and performance in all LLM-based systems.
Manage technical complexity and connect infrastructure efforts with clear business value.
4+ years of product management experience, ideally with a focus on AI/ML, NLP, or infrastructure/platform products.
Strong technical background in ML infrastructure, data systems, or engineering;
Experience building or scaling systems based on large language models or unstructured data processing.
Strong understanding of the AI/LLM ecosystem – including model deployment, evaluation, prompt orchestration, and data pipelines.
Proven track record of leading cross-functional teams to deliver technically complex products with measurable impact.
Excellent communication and collaboration skills, especially in technical environments.
Ability to operate in ambiguity.
Ability to drive clarity through structured thinking and sharp execution.
Background in ML infrastructure, AI products, or applied research is a plus.
