• Architect, build, and maintain Jump’s internal AI platform — including model evaluation frameworks, experimentation, context and prompt management systems, and fine-tuning pipelines. • Standardize and automate internal tooling: from Claude code org wide setup to orchestration frameworks for AI agents and workflows. • Design and deploy RAG systems, vector databases and other foundational components for production-grade AI systems. • Define best practices for evaluation and benchmarking of models and agentic workflows (including quality, reliability, latency, cost, and safety). • Develop templates and libraries for agentic workflows, memory, and tool use. • Partner with the engineering organization to integrate AI agents and features into Jump’s products. • Ensure reliability, observability, and scalability of AI systems across environments. • Act as an AI subject matter expert — running workshops, demos, and internal talks to help every function apply AI to their workflows. • Lead rapid experimentation initiatives — identify and prototype high-impact internal AI use cases, then scale successful ones. • Partner cross-functionally with HR, Finance, and Client Services to design and deploy AI automations that save time and increase quality. • Maintain an internal AI playbook — a living repository of best practices, design patterns, and how-to guides for prompt engineering, context design, agent building, and evaluation. • Mentor engineers and product teams on effective use of LLMs, model selection, and architecture design.