Senior Machine Learning Engineer

SageSure•Jersey City, NJ

2d•Remote

About The Position

If you're looking for the stability of a profitable, growing company with the entrepreneurial spirit of a startup, we’re hiring. SageSure, a leader in catastrophe-exposed property insurance, is seeking a Machine Learning Engineer. As a Senior Machine Learning Engineer, you'll play a crucial role in optimizing orchestration processes and ensuring fast and efficient model deployment and delivery. You'll work closely with Software Engineers and Data scientists to streamline machine learning pipelines and implement best practices for managing and deploying ML models.

Requirements

Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical field.
5-7 years of experience as an MLOps Engineer or similar role, with a proven track record of optimizing machine learning pipelines and infrastructure.
Proficiency in cloud computing platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
Experience with orchestration tools and frameworks such as Airflow, Kubeflow, or MLflow.
Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-Learn.
Experience in deploying machine learning models in production environments and managing model lifecycle.
Excellent problem-solving skills and ability to work independently as well as part of a team.
Strong communication skills and ability to collaborate effectively with cross-functional teams.

Responsibilities

Design and implement robust, scalable, and efficient data pipelines for training machine learning models.
Develop prediction pipelines to ensure seamless integration of trained models into production environments.
Create APIs and microservices to facilitate communication between machine learning models and other software modules.
Design, build, and manage model deployment strategies to ensure reliability, scalability, and security in production environments.
Implement monitoring and logging solutions to track model performance, data quality, and system health in real-time.
Optimize orchestration processes to ensure efficient deployment and management of ML models.
Implement cost-saving strategies to minimize infrastructure expenses while maximizing performance.
Collaborate with cross-functional teams to identify bottlenecks and implement solutions to improve workflow efficiency.