Computer Vision & MLOps Engineer

Basel, CH

ARTIDIS

ARTIDIS, a proprietary nanotechnology platform paired with advanced analytics, can be applied to any living tissue providing a broad range of R&D applications

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About ARTIDIS

ARTIDIS AG is a clinical-stage health-tech start-up founded in Basel, Switzerland, that has developed the first nanomechanical biomarker for cancer diagnosis and treatment optimization. The ARTIDIS nanotechnology platform integrates different types of clinical data into the ARTIDISNET digital platform, allowing physicians to significantly shorten the current diagnostic process and benefit both the patient and the healthcare system. ARTIDIS enables professionals to design personalized cancer treatment plans tailored to individual patients' needs and desired outcomes.

Job Purpose

This role combines deep technical expertise in computer vision with strong MLOps capabilities to build and maintain production-grade AI systems.

Duties

  • Develop and deploy computer vision models for:
    • Medical image classification
    • Advanced segmentation
    • Complex image analysis
  • Implement solutions using deep learning and classical techniques
  • Optimize models for medical device and cloud deployment
  • Design and implement end-to-end ML pipelines
  • Manage model lifecycle from experimentation to production
  • Build, automate and maintain scalable MLOps infrastructure
  • Implement robust monitoring and observability solutions

Qualifications

Technical Skills - ML & Computer Vision:

  • Proficiency in Python data stack (TensorFlow, PyTorch, numpy, pandas, scikit-learn).
  • Experience with OpenCV and other relevant computer vision libraries.
  • Strong background in computer vision solutions for medical images.
  • Expertise in developing and implementing computer vision solutions mainly image segmentation and object recognition.

Technical Skills - DevOps & MLOps:

  • Experience with Kubernetes operators and custom resources.
  • Proficiency in Docker containerization and multi-stage builds.
  • Advanced Git knowledge (branching strategies, workflows).
  • Experience Building and maintaining end-to-end ML pipelines.
  • Hands-on experience with Argo CD and Argo Workflows for ML pipelines.
  • Experience with infrastructure automation tools like: Helm charts for Kubernetes deployments.
  • Experience setting up and maintaining CI/CD pipelines.
  • Hands-on experience with model lifecycle management.
  • Hands-on experience with MLOps platforms: MLflow, Kubeflow...

Professional Skills

  • High level of expression and comprehension of the English language.
  • Experience in digital health and driving innovation in healthcare is highly desirable.
  • Commitment to writing documented, clear, efficient, and maintainable code.
  • Ability to design and implement requirements with long-term objectives, scalability, and maintainability in mind.
  • Proactive way of communicating and working.
  • Ability to work independently and take ownership without extensive supervision.
  • Strong team player.

Working Conditions

The environment is a typical start-up with an intrinsically motivated international team. Our company is an excellent place for rapid advancement and offers a promising opportunity to learn and share know-how in a very agile environment. The position requires the employee to go the extra mile when needed. We are looking for a team player who appreciates direct communication. The effort is rewarded with challenging tasks, the possibility to take responsibility, a highly driven team, and work serving a significant purpose. The job position comes with a competitive salary and a bonus for outstanding performance.

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* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰

Tags: Agile CI/CD Classification Computer Vision Deep Learning DevOps Docker Git Helm Kubeflow Kubernetes Machine Learning MLFlow MLOps NumPy OpenCV Pandas Pipelines Python PyTorch Scikit-learn TensorFlow

Perks/benefits: Competitive pay Salary bonus Startup environment

Region: Europe
Country: Switzerland

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