AI Platform Engineer for Medical Learning

Augmedi; Zollikon; On-site; CHF 120,000 - 180,000; Full time

Job summary

Augmedi in Zollikon near Zurich seeks an experienced AI/ML software engineer to build platform features and bring AI agents into the product, while fine-tuning models for medical learning content in collaboration with the Engineering Lead.

You will implement features from the TypeScript frontend to the Azure Kubernetes Service backend, develop MCP-based agents, convert learning content into datasets, run fine-tuning experiments, and help enhance the team's development practices with reusable

Qualifications

• Master’s degree or PhD in computer science, data science, electrical engineering, physics or a related field (ETH Zurich or comparable).
• Fluent in German or English.
• Proficient in TypeScript and/or Python with demonstrable projects or code.
• Hands-on ML experience, e.g. PyTorch; fine-tuning/LLMs a plus.
• Strong data modelling and relational database knowledge.
• Independent, proactive contributor to technical decisions and clear communicator.

Responsibilities

• Implement features end to end from frontend to AKS backend.
• Build AI agents and tools for admin and customer use; evaluate reliability.
• Turn learning content into datasets; run fine-tuning experiments with baselines.
• Improve development processes with coding agents and reusable skills.
• Design and migrate data models for content, annotations and usage data, with Swiss data residency in mind.

Skills

TypeScript

Python

Machine learning

Data modelling

Independent working

German or English

Education

Master’s or PhD in CS/related field

Tools

Kubernetes

Azure

CI/CD

Job description

Augmedi in Zollikon near Zurich seeks an experienced AI/ML software engineer to build platform features and bring AI agents into the product, while fine-tuning models for medical learning content in collaboration with the Engineering Lead.

You will implement features from the TypeScript frontend to the Azure Kubernetes Service backend, develop MCP-based agents, convert learning content into datasets, run fine-tuning experiments, and help enhance the team's development practices with reusable