Build and deploy production LLM agents and automated workflows for business teams. Responsibilities include developing Databricks lakehouse data foundations, integrating SaaS and internal systems, implementing evaluation and monitoring, exposing APIs, creating reusable AI components, and ensuring privacy, cost, reliability, and maintainability. The role partners with engineering, data science, architecture, and business stakeholders, and includes documentation, demos, and occasional travel to New York.

Own and advance MLOps practices for real-time machine learning systems, including model deployment, serving, scaling, monitoring, lifecycle automation, and infrastructure optimization. Partner with ML scientists, data engineers, and Product to operate reliable low-latency models in Databricks and AWS. Establish engineering standards, resolve production issues, review designs and code, mentor colleagues, and track model performance, reliability, and costs.

Own and advance MLOps practices for reliable, scalable, cost-efficient machine learning systems supporting ad selection. The role operationalizes models in Databricks and AWS, builds automated training and deployment pipelines, establishes model governance and monitoring standards, designs low-latency serving infrastructure, resolves production issues, maintains performance dashboards, reviews designs and code, and mentors colleagues.