Senior ML Operations Engineer

What you'll do

• Design, build, and maintain scalable ML infrastructure and pipelines for model training, deployment, and monitoring.
• Optimize orchestration processes to ensure efficient deployment and management of predictive models.
• Optimize resource usage to minimize infrastructure expense while maximizing performance.
• Monitor and maintain the performance, security, and scalability of the ML infrastructure.
• Develop and maintain tools for data analysis, experimentation, model versioning, and artifact management.
• Create robust monitoring systems to measure model performance and detect model drift.
• Develop automation scripts and tools to improve the efficiency and reliability of MLOps processes.
• Provide technical assistance and mentorship to team members while troubleshooting complex issues.

What we're looking for

• Candidates must independently possess the eligibility to work in the United States for any employer.
• Completion of a Bachelor's degree in Computer Science, Engineering, or a related field is typically required.
• Minimum of 5 years’ experience in a Data Science, ML Engineering, or ML Ops capacity is required.
• Strong programming skills in Python and experience with Data Science and ML packages and frameworks are required.
• Experience with AWS services, containerization technologies (Docker, Kubernetes), and CI/CD practices is required.
• Experience deploying models with MLOps tools such as MLflow, Kubeflow, or similar platforms is required.
• Expert understanding of data management, distributed computing, and software architecture principles is required.
• Experience in hybrid (OnPrem / Cloud) environments, Hadoop/Hive/Cloudera, and Spark is preferred.