Our client is a medical tech company specializes in AI-powered echocardiography solutions.
Job Responsibilities:
- Al Engineering & Infrastructure
- Build and maintain ML infrastructure and tooling to support our data science teams model development
- Develop robust data pipelines for training validation and continuous model improvement workflows
- Create automated testing frameworks for model validation and performance monitoring
- Optimize model performance for inference speed memory usage and scalability in production
- Support data scientists with engineering tools and frameworks that accelerate their research and experimentation
- Deployment & Production Systems
- Deploy Al models to production with robust monitoring logging and performance tracking systems
- Collaborate with engineering teams to integrate Al capabilities into broader system architectures
- Implement comprehensive MLOps practices for model versioning testing automated deployment and rollback procedures
- Build scalable inference systems that can handle high-volume medical imaging workloads with consistent performance
- Ensure model reliability and maintain high uptime for critical healthcare applications
- Create deployment automation that enables data scientists to easily promote models from development to production
- Innovation & Improvement
- Stay current with Al trends and evaluate new techniques for potential integration
- Suggest improvements to existing systems and workflows based on latest research Participate in research initiatives and potentially contribute to academic publications
- Mentor junior engineers and contribute to building our Al team culture
- Collaborate with clinical partners to understand domain requirements and validate model performance
Job Requirements:
- Degree in Computer Science Engineering or related field with focus on machine learning/Al Deep expertise in computer vision with proven experience in image classification and segmentation
- Strong Python proficiency for ML development data processing and system integration
- Experience with Al frameworks such as TensorFlow Keras and PyTorch
- Problem-solving mindset with ability to debug complex distributed ML systems