We are building a privacy-first MLOps platform for data-driven organizations in healthcare and life sciences. The platform is designed to support the entire lifecycle of machine learning (ML) efforts to accelerate breakthrough medical research and bring clinical-grade ML solutions to market. Our fast-expanding strategic network includes early clinical and technology partners and organizations in the US, Israel and Europe.

Responsibilities Help conceptualize and build an MLOps platform around data management and federated learning; from initial design to full implementation and deployment

Work with the team to design and implement tools and APIs for a centralized system with distributed agents/workers

Build supplementary software components that enables data scientists to interact with the platform

Support integration with existing ML/DL/FL libraries

Develop highly scalable machine learning (computer vision)



models to solve problems such as medical image classification and segmentation

Develop in-house machine learning tools and pipelines to support quick experimentation of machine learning models

Work with other engineers to identify and solve machine learning problem Requirements Experience in one or more of the following areas: deep learning, computer vision,

Experience with machine learning frameworks such as TensorFlow, PyTorch or YOLO

Curiosity and minimal experience in Federated Learning & Self- Supervised Learning algorithms & applications

Expert knowledge in Python (object oriented design)

Expertise in API design with FastAPI

Through understanding of deploying ML models via Docker and Kubernetes at scale on-prem and cloud.

📌 Machine Learning (Remote) (Jammu)
🏢 Gesund.ai
📍 Jammu