AI/ML Engineer for Education SaaS

We are seeking an experienced AI/ML Engineer to re-engineer and scale "SA Note," an innovative AI-powered medical education platform designed for UK medical students preparing for high-stakes board exams (specifically the PLAB-2). Our vision is to consolidate comprehensive UK medical textbooks and exam materials into a single, lightning-fast study tool, eliminating the need for students to juggle multiple resources. The project involves taking over an existing, stalled development build and completely re-engineering it from the ground up to create a production-ready SaaS powerhouse. A major component of this role will be building and integrating state-of-the-art Large Language Models (LLMs) and AI voice agents that can realistically simulate patients, allowing students to practice clinical scenarios dynamically. Responsibilities include: Architecting a scalable, high-performance AI infrastructure tailored for rigorous medical education use cases. Developing and deploying AI voice agents capable of simulating real-time patient interactions for PLAB-2 prep. Integrating leading LLM providers and implementing conversational memory and context management for continuous student-AI interactions. Creating a robust data ingestion pipeline to securely manage and query extensive medical textbook databases and exam materials. Developing secure user authentication, subscription management systems, and scalable APIs. Building administrative tools, analytics capabilities, and cloud infrastructure to support a rapidly growing user base of medical students. Technical Requirements & Experience: Required skills include Python, FastAPI, Generative AI, LLM Integration, RAG Systems (crucial for textbook data retrieval), Vector Databases, AWS Cloud Infrastructure, Docker, PostgreSQL, React/Next.js, REST APIs, SaaS Architecture, and AI Application Development. Experience in the EdTech space, specifically with medical education, high-stakes exam prep, or AI voice agent development, is highly preferred. We need a team or engineer capable of rapid turnaround, taking a complex system from failing to fully optimized within a tight timeframe (e.g., 4 months). Final Deliverables: A fully functional, re-engineered AI medical education platform, scalable cloud deployment, live AI patient simulation capabilities, a centralized medical resource library, user and administrative dashboards, production-ready infrastructure, complete source code, and technical documentation.