Build end-to-end AI features: Architect and ship fullstack solutions.
Operationalize Voice AI: Implement and fine-tune audio processing pipelines.
Bridge the gap between model and product: Translate complex feedback from clinicians into technical solutions.
Optimise for real-time interaction: Tune fullstack performance for real-time audio streaming.
Partner with implementation and clinical teams: Shorten the feedback loop by shipping critical integrations and feature requests.

Mastery of Fullstack fundamentals: You are equally proficient in Python and modern frontend frameworks (React/TypeScript).
Applied AI & Voice fluency: You have a working knowledge of LLM integration (RAG, prompt engineering) and audio technologies (ASR, speech processing).
Pragmatic problem solving: You balance engineering purity with the need for speed.
Cloud fluency (AWS or GCP): You can spin up your own infrastructure (containers, serverless functions).
Rigorous testing in production: You understand that works on my machine isn't enough; you implement observability and feedback loops.
Medical degree with clinical experience, and ideally experience working on clinical AI products.

Clinical AI Engineer

Key skills