Optimize orchestration processes to ensure efficient deployment and management of AI models.
Implement cost-saving strategies to minimize infrastructure expenses while maximizing performance.
Upgrade throughput to enhance scalability and responsiveness of AI systems.
Collaborate with cross-functional teams to identify bottlenecks and implement solutions to improve workflow efficiency.
Ship new features and updates rapidly while maintaining high levels of quality and reliability.
Deploy and monitor machine learning models produced by deep learning engineers.
Design, deploy, and maintain performant and scalable processes for data acquisition and manipulation to enhance dataset accessibility.
Participate actively in the team's software development process, including design reviews, code reviews, and brainstorming sessions.
Maintain accurate and updated software development documentation.

Bachelor of Science or Engineering degree or a foreign equivalent in Robotics, Machine Learning, Computer Science, Electrical Engineering, Electronics and Telecommunication Engineering, or a related field.
One (1) year of work experience in the job offered or as a Deep Learning Engineer, Perception Engineer II, MLOps, Research Engineer II, Machine Learning, Computer Vision Intern, or another related deep learning engineer role.
One (1) year of work experience with all of the following:
Deploying real-world applied computer vision (including deep learning models) on edge devices
Python programming and software design
C++
Software development tools and libraries including PyTorch, OpenCV, TensorFlow, MLflow
Automated data annotation
Distributed training in the cloud
Deploying and managing GPU clusters for ML pipelines and workflows

Machine Learning Operations Engineer

Key skills