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 team's 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