Ml Engineer

Responsibilities

Take ownership of the entire machine learning engineering lifecycle — from building scalable training and evaluation pipelines to deploying models in production, with robust monitoring and maintenance systems.

Help in creating scalable solutions by enabling us to continuously increase the accuracy of our algorithms across thousands of clinics.

Designing a secured large-scale system that is suitable for sensitive patient data.

Continue to enhancing our deep learning infrastructure to supports our AI models at scale, including CI/CD, automation, testing and monitoring.

Collaborate with the research, medical and product teams in implementing ML solutions to the digital health space.

Requirements

4+ years of hands-on experience in software engineering (Backend preferably in Python).

2+ years of experience in machine learning pipelines on cloud environments.

Knowledge in statistics and machine learning techniques.

Proven ability to lead product feature development, from concept to production.

Experience with large scale, high performance, production environments.

Experience working with SQL and NoSQL databases.

Experience in AWS cloud environment.

Advantages:

Bs.c / Ms.c in Computer Science / Software Engineering.

Experience with ML Frameworks such as PyTorch, TensorFlow and MLFlow.

Experience with Deep Learning, NLP and LLM pipelines (RAG and agentic systems).