Senior Machine Learning Engineer

SageSure

$120K — $150K *
Information Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • 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.
  • Proficiency in cloud computing platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes).
  • Experience with orchestration tools such as Airflow, Kubeflow, or MLflow.
  • Familiarity with machine learning frameworks like TensorFlow, PyTorch, or Scikit-Learn.
  • Experience in deploying machine learning models and managing the model lifecycle.
  • Excellent problem-solving skills and strong communication abilities.

Responsibilities

  • Design and implement robust, scalable, and efficient data pipelines for training machine learning models.
  • Develop prediction pipelines for smooth integration of trained models into production.
  • Create APIs and microservices for communication between models and other software components.
  • Manage deployment strategies to ensure reliable and secure model hosting.
  • Implement real-time monitoring solutions for model performance and data quality.
  • Optimize orchestration processes for efficient model management.
  • Collaborate with teams to identify bottlenecks and propose workflow improvements.

Benefits

  • Generous health benefits and wellness allowance.
  • Tuition reimbursement for continued education.
  • Paid volunteer time off to support community engagement.
  • Matching 401K plan to aid in long-term financial planning.
Full Job Description
Overview:

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.

What you'd be doing:
  • 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.

We're looking for someone who has:
  • 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.

Data at SageSure

At SageSure, data isn't just a function-it's a force for innovation. Our Data organization brings together Data Science, Business Intelligence, and Data Management to power smarter decisions and deliver meaningful impact across the business. From predictive modeling to scalable data ecosystems and actionable insights, we turn complex information into clarity that drives real results.

We're building a team of curious, collaborative problem-solvers who are passionate about using data to make a difference. Whether you're advancing machine learning capabilities, strengthening data governance, or uncovering insights that shape strategy, you'll play a critical role in defining how SageSure uses data to lead the future of insurance.

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