• Prototype to Spec: Instead of writing abstract requirements, you will build functional prototypes using tools like Streamlit, LangChain, or Python notebooks to validate feasibility. You verify prompt strategies in playgrounds (OpenAI, Anthropic, Vertex AI) before engineering writes a single line of production code. • Bridge the Technical Gap: Translate 'model constraints' (context windows, inference costs, probabilistic outputs) into concrete product mechanics. You will sit with engineers to debug edge cases, not just report them. • Own the 'Applied' in Applied AI: Drive the transition from 'model performance' (F1 scores) to 'product performance' (user success rates). You will define what 'good enough' looks like for production. • Wrangle Complex Data: Work hands-on with messy, real-world healthcare datasets (claims, EHR, patient-reported data). You view data quality not as someone else's problem, but as a core product asset. • Design for Trust, Speed & Explainability: Collaborate closely with Compliance, Design, and Engineering to build UIs that help users understand AI outputs, handle probabilistic edge cases gracefully, and foster user trust in the system. You also understand the critical tradeoffs between model accuracy and speed to insight, and design with this in mind. • Ship Responsibly: Design the guardrails, fallback mechanisms, and human-in-the-loop workflows that make AI safe and compliant in a healthcare setting.