In October 2024, we announced LinkedIn’s first agent, Hiring Assistant to a select group of LinkedIn customers. Designed to streamline a recruiter’s workflow, Hiring Assistant automates time-consuming tasks, from intake to interviews, allowing recruiters to focus on the most strategic and people-centric aspects of their roles.

Recruiters can upload job descriptions, intake notes, and job postings to the Hiring Assistant, which then uses this information to generate role qualifications and build a pipeline of qualified candidates. Throughout the process, recruiters remain actively involved, and can provide guidance to the Hiring Assistant about its actions to ensure alignment. This continuous feedback loop enables Hiring Assistant to learn each recruiter’s preferences, becoming increasingly personalized and effective over time.

Building this product required pushing the boundaries of using Large Language Models (LLMs) for large-scale automation, achieving personalization at scale while maintaining product quality and upholding our responsible AI principles, and pioneering new UX paradigms for human-agent interaction. This talk will provide an overview of the Generative AI product development evolution at LinkedIn, culminating in the most technically complex product built at LinkedIn using this tech, the Hiring Assistant. We will then delve into the system architecture of the Hiring Assistant, highlighting key design decisions made to ensure efficient scalability, security, robust operability, and thorough quality evaluation. The talk will conclude with key lessons we’ve learned, offering valuable insights for anyone building real-world, production-grade AI agents.