AI/LLM Engineer for Self-Hosted MedTech AI Platform

Project Overview We are looking for an experienced AI/LLM Engineer to support the development of the AI component of an existing MedTech platform. The MVP and core application are already in place, and we now require an expert to design and implement a private, self-hosted AI solution. Due to the sensitive nature of medical data and compliance requirements, we cannot use public AI APIs such as OpenAI, Google Gemini, or Anthropic Claude. The solution must be built using self-hosted or open-weight models, ensuring that all data remains private and secure. Responsibilities Evaluate and recommend suitable open-weight LLMs for the use case. Design and implement a self-hosted AI architecture. Fine-tune or customize models where required. Build AI APIs and integrate the solution with the existing platform. Implement secure data handling and deployment practices. Collaborate with our existing development team and CTO. Required Skills Strong experience with Python and AI/ML development. Experience with Large Language Models (LLMs) and open-weight models. Hands-on experience with frameworks such as Hugging Face Transformers, PyTorch, and model fine-tuning techniques (LoRA, QLoRA, PEFT). Experience deploying and serving self-hosted models using tools such as vLLM, Ollama, or similar technologies. Experience designing Retrieval-Augmented Generation (RAG) solutions is a plus. Familiarity with cloud infrastructure, Docker, Kubernetes, and GPU environments. Experience working with healthcare or regulated data environments is highly desirable. Engagement Model Part-time, ad hoc engagement based on project requirements. Ability to collaborate with an existing team of developers and technical leadership. Potential for a long-term relationship as the product evolves. Preferred Experience MedTech or Healthcare AI projects. Building secure, private AI systems. Deploying and managing self-hosted LLM infrastructure. When applying, please share: Similar AI or self-hosted LLM projects you have worked on. Experience with healthcare or compliance-sensitive applications. Your preferred technology stack for building private AI solutions. Availability for ad hoc engagement and your hourly rate.