Landon Kleinbrodt

LLMs & Agentic Systems - Architected and implemented a product-intelligence agent that uses hybrid RAG and tool-calling LLM agents to answer deep research questions about the product suite. Owned AI design and implementation while partnering with product, data, and UX on requirements, data contracts, and user experience. - Integrated a deterministic portfolio construction framework with LLM agents to deliver automated recommendations and diagnostics, enabling ~4x faster portfolio consultations. - Fine-tuned open source LLMs to extract actionable insights from unstructured data (sales notes, marketing materials, etc) enabling better understanding of client priorities. ML pipelines, predictive modeling, and financial analysis - Designed and implemented robust, end-to-end ML pipelines (data engineering, model training, lead delivery, experiment design, CI/CD, monitoring) that power a global sales organization. - Invented a proprietary portfolio construction framework aligned to the firm’s investment philosophy and led a team of four to build the production platform around it. Automated recommendations achieved ~80% approval from subject matter experts. - Owned a ML lead generation pipeline delivering monthly sales leads with data-driven insights to 200 salespeople, driving an estimated $1.2B+ in annual sales impact. - Helped define and evangelize team-wide AI guiding principles and best practices (trust, safety, composability) as the team transitioned to an AI-first charter.

• Developed predictive models to optimize the wholesale of mutual funds (using R and SQL) • Collaboratively designed plan to improve company's event interactions with financial advisors • Presented both projects to upper management and aided in their deployment

• Teach R language to undergraduate students in lecture, small group, and one-on-one environments • Oversee grading of 100 students and mediate interactions between students, instructors, and professor